good morning good afternoon andpotentially good evening everyone we areso excited to welcome you today todecoden diversity insights and Trendsabout the US tech Workforce brought toyou by the tech accountability Coalitiona program of Aspen digital which is aprogram of the Aspen Institute so firstI’d love to walk you through the agendafor today we’ll do a brief welcome andoverview of the Coalition followed bywhat you’re all excited to hear which isa data presentation sharing some of ourtop lines and sort of most interestinginsights that we’ve discovered from thiswork then we’ll dive into an amazingpanel discussion uh followed by someopen Q&A and closing remarks so withthat you might be wondering what is thetech accountabilityCoalition the Coalition was an outgrowthof something uh called the ACT report in2021 a group of of over 30 techcompanies and Tech leaders aligned on amore cohesive strategy one that wasabout making the EI a lens by which abusiness operated and not a thing thatHRdoes and the idea was that if we couldget all these companies aligned we couldactually start to make transformativechange in the tech sector all of thoseefforts translated into the action tocatalyze tech report or act reportthe CEOs of the ACT signatories pledgedto three things first to develop astrategy to implement therecommendations outlined in the ACTreport it’s super robust veryoperational highly recommended toattendees to check it out they alsoagreed to share their Workforcerepresentation data in Aggregate andanonymously to better track progress ondiversity because the reality is we sayDei but I think we’re forgetting thatthe d stands for diversityand finally the idea was that all ofthese companies were going to worktogether to support Collective actionand joining conversations to align onDei data collection and other reallycritical areas of cross industrycollaboration so with that in 2022 thetech accountability Coalition was formedsomething the report literally calls outis that so many companies sign on tothese pledges and commitments butthere’s no real accountabilitythe report calls out that there needs tobe an external accountability mechanismto hold those initial signatories totheir promises and to invite morecompanies into the fold and that’s uswhat we focus on are holding companiesaccountable to those initial commitmentsand honestly trying to remaincontemporary with the needs that we seein the tech sector around Dei we work toengage other tech companies fromstartups to some of the biggest techcompanies in the globe on the power ofcollect Ive action because we know thatworking together we can go further andfinally we work on the Aspen Instituteside of the house to provide technicalassistance and capacity building supportfor tangible change in the industry weknow that those leaders who arecommitted to this work within companiesare often overwhelmed sometimesunderpaid and we want to be able to be areal thought partner and anaccountability partner in thiswork our theory of change which was forformated with significant and gratefulsupport from deoe is that we want toconvene and provide subject matterexpertise for our participants andmembers specifically we facilitateenable and drive accountability fororganizations to accomplish in thepursuit of equity together what theycould not individually accomplishthemselves you see tech companies reallylike a lot of corporations are taught tocompete with each other and we do notbelieve that Dei and therefore TechEquity is proprietary in fact workingtogether we can address some of the mostsignificant challenges that all of thesecompanies arefacing so you might be wondering howdoes this allwork participants have the chance toco-create work products and developleading practices and the way we do thatis first we involve member techcompanies in the space we have anadvisory Council of tech industryexperts who have worked many of whomhave worked within Tech um and all ofwhom work adjacent to Tech who can giveus real perspective on how the effortsand accountability mechanisms we have inthe Coalition translate to directlyimpacted communities and finally weinvite key industry stakeholders such asCivic institutions and governmententities to be a part of this work ourgoal is not to be redundant but to infact bring together all of the alliancescoalitions and efforts around TechEquity so that again together we can gofurthersome of you might be wondering like whywould I join another Coalition of coursewe like to think about ourselves alittle bit differently in that we’rethinking about accountability anddriving in partnership with techcompanies but here are some keyhighlights of joining the Coalitionoverall of course there’s communitybuilding right Dei is not proprietary sowhether you’re working on product orculture or Pathways into Tech perhapsdata or policy work around Equity wewant to share ideas across aisle andthink about how we can collaborate withpublic and private sectors to drive Deiin the tech industry the Aspen Instituteis a global Think Tank focused oncreating a just and Civic society and sowe know how to convene right we’rereally good at helping youve reachpolicymakers media and peers that arealso leading on TechEquity we also know that today’sWorkforce is particularly keen onunderstanding if their personal valuesalign with the companies that they’reworking in so being able able to saythat you’re part of a coalition oftalent that is committed to Dei isdefinitely one way to up yourRecruitment and talent acquisitionefforts and finally industry recognitionis really important we understand thatyou’re investing your time talent andresources you want to know what fruitwill it bear as I mentioned we try towork with other alliances and coalitionsin this space and kind of create anumbrella of opportunity and engagementbut we do see ourselves as sort of firstpioneer and trying to think about how wehave companies moving beyond competitionto collaboration to drive forward thischange and so you would have the chanceto be sitting at that table and set itif you join uh the Coalition as amember finally we want to encourage youall to think about how you invest intransformation of the tech sector byworking with us the industry is rightfor Change and it is better served ifwe’re all working on this together ifyou have any questions about theCoalition how to become a member or anyof the other work that I’ve mentionedtoday please just reach out to us afterthewebinar so I know the moment you’ve allbeen waiting for show me the data and sowith that I’m so excited to Welcome tothe virtual stage Shrea Singh Hernandezwho is the coalition’s research managerand Joseph ifu who’s a co-founder andCEO of Equitable an outstanding partnerin our work but also an amazing HRanalytics firmthanksy’all amazing thank you Shu as you mayremember at our March convening lastyear the tech accountability Coalitionlaunched unbiased the future the equityframework with input from hundreds ofengagements across Tech sector leadersand Civil Society organizations thisresource offers a practical approach tosetting industrywide standards andWorkforce representation including datacollection progress tracking and sharedterminology when we published thisframework you if you recall one of thepromises companies made was to sharetheir representation data we partneredwith Equitable as our HR analyticspartner to ensure and source and analyzethis data at scale to create theinaugural data site you see todaypassing it over to Joseph ifu who willwalk you through our methodology and toplines thanks R so for the analysis wehad to first of all create a benchmarkuh we understand that any sort of changestarts with understanding where you arecurrently and so we started with pulling3.2 million Global employee datarecords uh for the year of2022 in addition to that we also pulledthe or that for CNN reports fororganizations where they had itavailable as well and in addition tothat we took the the 47 companies thatwere part of thatCoalition and we also analyzed that dataso we looked at 47companies The Source data in addition tothe dos and 23 of those companies haddos and then when we broke it downfurthermore we looked at 1.1 million ofus employee recordsanalyzed reason for you looking at us isbecause again when you start looking atglobally it starts to become morecomplex when it comes to the diversitybreakouts and one thing to note as wellis that obviously in the last year totwo years there was a there’s been a lotof layoffs and all of that for2022 the data um wouldn’t really reflecta lot of that because all over time umthe changes in terms of the publiclyavailable data uh does it doesn’t reallyreflect early enough right let’s say youyou left a job today you might not youmight not have that um shown publiclyuntil three or four months down the lineas well so just some cave kind of havein mind in terms of the year that we hadthe datafor the next slideplease and so limitations of the dataset um because it was a publicly sourceddataset gender identity is limited to thatto the binary options that you see inthe typical reports right like the eo1report soagain we this is the starting point andthe purpose of this is that over time itwill serse the foundation for change butwe started with this data set so we hadthe binary options so man women the raceis limited to the six eo1 Catecategories aswell um sexual orientation is notavailable because again it’s not aself-reported um data set as well sothat wasn’t available in this firstanalysis that we did and a lot of thecompanies that we pull the data for donot have intersectional data so when welook at some of the DARS as well so thatintersectional data was missing againbut this is all this is all good thingsbecause again we know okay this is wherewhere to start and then you can only goup from here and then finally we couldwhen we looked at the publicly sourcedversus the DARS there are somediscrepancies again tied to the point Isaid earlier on where something mightnot be reported as soon as they happenso when you pull data from from thepublicly available information theremight be some things that are missinghowever directionally uh it gives us avery strong picture in terms of what’sgoing on in the in the tech industry aswell okay so when we looked at the dataone of the first things we looked at wasthe gender representation I I don’tthink this comes as a surprise um to toto all of us but again this is thingsthat we’ve been seeing for a while thebreakdown for gender um men versus womenwas 61 to about 39% now note thatthere’s anotherum breakout and this is this isessentially people that we couldn’t umget that the categoryfor of course in reality these numbersare going to be different because thereare going to be other multiple gendercategories but for the purposes of thisit was a 61 to39% breakout so men were almost twice umas women in terms of representation inthe tech Workforce industry as awhole next slide please and then when welook at race at a higher level thebreakout again um white category Iaccounted for 61% of overallrepresentation followed by aapi um andagain this is this is this these metricsand this this uh just data points aresomething that been consistent over theyears so this something that we’ve seenso again it comes as no surprise butthis is just something that we saw whenwe pulled that um publicly sourced dataso wi 61% aipi 2023.1 and then black and and Hispanicwere were um close after with 8.9 and6.3 aswell and then and we looked at theleadership which is always importantbecause again leadership when you thinkabout the culture of an organization anddecision making it’s important tounderstand who are at the top of thoseuh of of of the decision making who hasthat power to make decisions in anorganization and so in looking at thedata points only about 6% of theworkforce make it to director level andof thosedirectors about 58% of them weremen and then of those directors as wellwhen we look at the the race categoriesum almost half so about 47% of them werewhite followed closely by aapi whichaccounted for about 31% of that and soagain we we keep seeing the same sort ofthemes where um category white dominatesin terms of the category breakouts andThen followed closely by aapi and thenyou have Black andHispanic um as the other kind of smallerum minority but however that’s what wekept seeing continuously as a trend aswell thank you Joseph um so ourreporting found that despite years ofintentional training recruitingleadership development programs Etcwomen still remain significantlyunderrepresented in the tech Workforceas a whole and particularly in technicalroles our data shows that only38.5% of the total work Workforce arewomen and of this 44% of non-technicalWorkforce are women but only 25 25.5% oftechnical Workforce arewomen this discrepancy worsens whendisaggregated by race on the next slideum white women are 2.5 times more likelyto be in a tech role than black LatinaHispanic and Native American women willshort form that as BNA womenAsian-American and Pacific Islander oraapi women are two two times more likelyto be in a tech Ro BL and women’srepresentation in the tech Workforcedeclined from4.6% in 2018 to 4.1% in 2020 even thoughmore BNA women are graduating withtechnical degrees as our partner rebootrepresentations latest data showsalthough BL women are earning compComputing degrees their share of thetech Workforce is going down this islikely due to a number number ofcultural factors including experiencingPrejudice at work a lack of retentionsupport services and programming andoverall bias in hiring and promotionpolicies this failure to disaggregate ismisleading and harmful for manyunderrepresented groups for example Dentartificial subdivision in race andethnicity and a supremacist view onracial identity asian-americans andPacific Islanders have historically beenexcluded from definitions of racialgroups um under representation regardingpeople of color coupled with frequentstatements about over representation ofaapi workers in the tech Workforce whencompared to their percentage of the USpopulation many companies havecompletely ignored the need forrepresentation at parity for the APIcommunity in our review of DS we foundthat the Strategic inclusion of aapis insegments that include other people ofcolor can also often invisibilize theexperience of other groups of color byinflating metrics of success wereinconvenient additionally to segregationof groups beyond the conglomerate ofaapi May reveal vast disparities inrepresentation and the advancement ofdiverse ethnic groups for example Mworkers Vietnamese Tech workersMongolian Tech workers to name a fewthey are invisibilized by this umbrellalabel consequently when you disaggregatethe data by Department Tech versusnon-tech level and gender you’ll seethat API women here have a similar rateof promotion to their counterparts butit does not move forward the parity ofrepresentation given the largerepresentation of women in the directorand Below levels you’ll find here that44% of the largest racial group uhpresent in technical roles is APIcommunities um but of that39.5% are API men and 36% of thetechnical Workforce are APIwomen on the next slidewe’ll see that for non-technical workers67% are white and44.4% are men when looking at directors46.2% are white men and 62.2% are whitewomen you can’t continue to see realculture shifts within an organization ifyour culture Setters I.E those in non-Tech side of the house including HRTalent acquisition compensation andbenefits finance and employment andlegal aren’t representative of a moreracially and gender diverse companythese leaders make decisions oneverything from hiring to budgetallocation I’m going to now hand it backto Joseph to talk about um comparing ourdatasources um so we looked at hiring andbefore we compare to their sources welljust one more slide we looked at hin andhow that affects representation as awhole because we’ve talked aboutrepresentation this whole timeand what we found was of the 63% of newhires that were for non-technical rolesuh 51% were women and 48% were men butwhen we looked at the tech roles whichwere which accounted for 37% of newhires we saw a flip whereby you hadmajority I mean it was like we talkingabout three exp 3x men so we had 73%were men and 27% were women and this isfor Tech R specifically so showing thatthere has there’s still a lot of workthat has to be done on the tech Ro sideof the house across organizations in thetech industry and then when we looked atthe leadership role so men like in termsof being in terms of leadership as awhole white men were being hi toleadership uh at 51% compared to therest of the compared to the rest of themen in the organization so when youlooked at let’s say 100 men werehired into an organization 51 of themwere white men and and the same thinghappened with director plus level sowhen we look at director plus of all themen that were hired into director pluslevel 52% of those were white men aswell so again there’s still a bit ofwork that we saw that has to be donefrom that perspective and then we haveto now compare this data so we looked atrepresentation and we looked at hiringand so let’s we compared the data thatwehad from the publicly sourcedversus Dar and so we took we took a wetook a random company we did this forevery single company but this is just toshow you a random company that we tookand saw the discrepancy between what welooked at publically versus what theypublished in the da report so forexample and the representation for theworkforce black African-American theworkforce said our data said6% but in the DAR it was about 4% so yousaw that 2% discrepancy againdirectionally tells a story but we startto see a bit of discrepancy and thiscould be again due to reporting andthings like that in terms of the whitemen versus uh in terms of the workforceversus D we saw 32% in our data and thentheir D set 25% again a bit of adiscrepancy there and then white womenour data set 177% and then again the Dsaid 15% so there was some slight umdifference in data in data points butagain again it tells that directionalstory see the next slide and then welooked at highers so on the other handlooked at representation but we alsolooked at highers as well um aapi womenour data set11% D set17% and then for multer ratio our dataset zero or close to zero essentiallyand then their data set about 3% soagain these are things that we we had tolook out for while we’re telling thestory but I want to reiterate everythingit was still directional and tellingthat exact story in terms of the trendsthat we seeing in the organizations backto yoush amazing thank you again for yourpartnership Joseph better data leads tobetter Justice and withoutstandardization we cannot make theprogress that we all have committed towe welcome you and your team to checkout the equity framework on our websiteand to reach out to the techaccountability Coalition with anyquestions I’m excited for the panel thatwe have um coming soon and and handingit back toshuhi everyone um you know I’m excitedabout those top lines I will say thatwhen we looked at all of the employeerecords that were publicly sourcedanonymized andaggregated we had over330 different data permutations and sotrying to decide to dissect it anddetermine which ones we thought would bemost Salient and not redundant wasreally hard but we really welcome yourquestions and challenges and pokingholes in it something I really want tounderscore that sha just sort of umhighlighted for us is that we are onlyas good in our data presentation as wehave access to good data and so becausewe had to do publicly sourced um recordsat this point if you recall Josephshared some of the limitations up topand that meant that we could only lookat for example gender as a binary whichwe know is false we could only look atsix racial categories according to theEEOC C data we know that’s also falsethe reason why we want you to considerthe equity framework and either adoptingor adapting this for your own datacollection within your organizations isbecause we as a larger society we as apeople we as workers in the techindustry should have greater Insightthat aren’t about what individualcompanies want to publish or vanitymetrics it should be about who’s reallyworking in Tech and so we would love tobe able to have greater intersectionalum um race data we would love to havegreater gender identity and sexualorientation data for example but we canonly do that if you all start collectingand sharing thisinformation so with that how do we thinkabout actions and strategies andrelevant approaches to the Deep datadisaggregation that we hope to see wellI have the extreme pleasure ofintroducing our panel who’s going totalk to us more about diversity buildingstrategies so first up I’d love towelcome Maya Opia who leads the globaldiversity equity and inclusion functionfor worldwide Amazon stores Talentacquisition her Team Works to equiprecruiting teams with Dei strategiesdata and mechanisms and works to upskillthem with Learning and Developmentopportunities because they recognizethat this information is a catalyst foradvancing Dei work at Amazon we alsohave leilo Ramirez who’s the director ofdeib at just works and just works as acompany on a mission to helpentrepreneurs and businesses grow withconfidence she’s also worked at Amazonweb services Netflix donah haareCorporation and management leadershipfortomorrow we’re going to welcome Josephback to the stage given the work thatEquitable does as an HR analytics firmpart of the reason the Coalition wantedto partner with them is because theyunderstood the value of intersectionaldata collection and Analysis so we’llhave Joseph back on and then we have theextreme pleasure of having D FranklinDavis who is the CEO of rebootrepresentation uh to moderate thisconversation danana is a lifelongtechnologist with a passion forincreasing diversity equity andinclusion in the tech sector and rebootrepresentation specifically focuses onincreasing the representation of blackLatina and Native American women in Techso I know there are more questions I seethem populating already in the Q&A butum if we could invite our panelists onto the virtual stage really lookingforward to the conversation we’re aboutto have and danana I’ll pass it to youto kick itoff thank you Shu I am so excited tohave this conversation um I am a data Iwant to say data nerd data geek I’m allabout the data um at reboot we know thatbeing able to leverage data to tell thereal story or an accurate story or evento have a foundationof knowledge so we know what to enhanceor improve is is key um and as we hadthe this this team this panel had theopportunity to get a sneak peek at thedata I don’t know that any of it waslike super surprising for those of usthat are entrenched in data but right Ialready see your heads nodding but Ithink the question is what are we goingto do with this knowledge now that wehave it and we have like real factsbehind what we already felt what hasalready been out thereso I’m excited to dive into conversationwith you all and we’re going to centerit around what are the challengesemerging Trends let’s get someactionable strategies out there um itwouldn’t be me if I didn’t talk aboutmeasurement and accountability so what’sthat look like and then obviously someof our our partners our companies outthere they’re Global so we want to throwthat Global Perspective out there it’snot just about the United States um andwhen and then as we talk about ourpresence what does it look like forintersectionality um the magic word soI’m going to dive in because I thinkthat we’re going to have a ton ofquestions out there um for those thatsubmitted audience questions ahead oftime I tried to weave in a couple ofthose so hopefully you’ll hear theanswers inside of the questions that arebeing asked um so Leila I’m gonna kickit off to you first um and the firstquestion is are there specificchallenges faced by minority groups ormarginalized communities in pursuingTech careers at yourorganization um first and foremost thankyou Zana for having me in um Techaccountability Coalition for yourincredible partnership andstewardship um this is I mean the datawould suggest that there are challengesthat um folks are facing um but I um youknow in thinking about how to addressthis question I would say like at justworks in particular we are veryfortunate um to be a younger company andto be able to like learn from thelessons of the companies that have gonebefore us and so when I think about umthe challenges that folks face um when Ithink about minorities and ormarginalized communities um it is veryconsistent right with what we see in thebroader Tech ecosystem and I would saythat as much as you know these are ourproblems to own internally um this isjust like a microcosm really of the muchbroader issue um but we’re fortunateenough that we are located in New YorkCity and that we have access to peoplewho are incredibly like talented andtrained at some of the world’s bestschools and so it may not be quite umquite as um quite as as as similar towhat we just sort of like looked at umwhen I think about some of the moreimmediate challenges they are consistentwith you know this idea of they’re notbeing um you know we could always bebetter in terms of representation interms of mitigating bias anddemocratizing access to opportunity butI would be remiss if I didn’t um itdidn’t say upfront that like you knowjust works as an 11y old company and soin many ways um while we are replicatingsome of what we see externally um I’mincredibly um a humbled and B justreally excited to be at a place wherewe’re not quite set in our own ways yetwe are still Building Systems andpolicies and so we have an opportunityto really build equitably from the startuh so I would say I mean that’s how Iwould address um where we’re at um so inthe same boat is what I would say yeahall right thank you for that I Iappreciate the fact that I’m gonna sayy’all are relatively new on the scene 11years is not that long but I think nothaving that Legacy infrastructure inplace allows you to be maybe a littlebit more Li Nimble maybe I would say soin some but you know change change atscill is difficult without structures inplace so absolutely absolutely Maya I’mGNA throw the next question over to youso what would you say are the primarybarriers that are preventing greaterdiversity in the techWorkforce yeah thanks um thanks Jannafor inviting me for this panel and andthe accountability Coalition as well forhosting this this space um I think whenI think about barriers um there’s kindof a few categories that come to mindthe first category that is critical andshould not be overlooked but I alsothink is sort of well discussed in thelast couple years is like these theseBaseline systemic issues right so whohas access to higher education who hasaccess to Computer Science Educationwhen we talk about technical careerswithin tech companies um who has accessto Internet in some cases and and forsome communities that’s even moreprofound um so there’s a lot of thosesort of systemic issues that contributeto the like quote unquote pipelineproblem that I think should not beoverlooked and there are a ton ofefforts I think to start getting umstart building that Pipeline and ensurethat there is more robust access toopportunity so I think that that’s onepiece but a couple of the areas that I’dalso kind of highlight that I think areless discussed um one is this idea ofthe network Gap right and so thinkingabout folks who are maybe first gencollege or first gen Cor corate rightand may not have a friend or colleagueor Aunt or uncle or parent or whateverwho has already sort of gone throughthat path and who cannot sort of brokerthat that conversation right and so thereason that this shows up and is soprofound is that there are sort ofunspoken rules still so when it comes tojob seeking right what’s the etiquettecan I reach out to this person onLinkedIn is that okay wait how do Inavigate a a career site that hasthousands of open rules who can tell melike how to apply for the right level oror position for me those kinds ofconversations so many times folks umunblock that barrier by knowing someoneinside and and they have someone who canwho can help them kind of navigate thesesort of unchartered Waters and so thatNetwork Gap I think becomes somethingreally critical for us to to think abouton the flip side I think for employersthat also creates sort of aresponsibility in the onus on them tobuild trust with these communities whomay not be thinking of them as anemployer of choice right so for exampleyou may know a you may know Amazon as aconsumer brand you may love to to shopthere but are you thinking about Amazonas a place to build your career are youthinking about that as a possibility forEconomic Opportunity for yourself oryour family that’s a different questionand so I I think many times employershave that opportunity and really thatresponsibility as we think aboutdiversifying our Workforce to say how dodifferent communities consider us andare we positioning ourselves as anemployer of choice as a place that wouldbe safe to build a career and that wouldcreate access to Economic Opportunity inin broader ways um the other barrierthat I I think of um and that I’veobserved particularly for newercompanies um is really thinking aboutthe structure and consistency and sortof codification within the recruitingprocess so that we really know who weare looking for right too many times youyou hear from job Seekers like I wentthrough this interview process and itwas 12 rounds and never ending andsometimes that’s a signal right that anemployer may actually not know whatthey’re looking for and that opens thespace for a lot more Affinity bias a lotmore um hiring someone who looks and andfeels like you and can just build thatRapport really easily which oftentimesmeans perpetuating similar types ofdemographics within that organizationand so that’s something that I wouldjust offer for for folks who um are indecision-making places in theirorganizations is like really think aboutif your hiring process has drivenClarity on the skills required to beable to do these jobs because if youdon’t have that Baseline in place it’sreally difficult to make an objectiveassessment and to create a fair andEquitable hiringprocess Maya I think that’s a great callout I think thatum there’s probably uh some some peopleout in the virtual audience that havehave gone through that I’ve had 12interviews and I’m in this black hole ofa process and I don’t know where I standum so I think that just helping thosethat are in that decision-making processmake no ahead of time before we gothrough the 12th interview where wherewe want to go with this I think willhelp all of us as we’re moving forwardwith our careers thank you for that Mayaso we talked a little bit about thechallenges um I want to move now intoemerging Trends and what does that looklike for what’s happening in yourorganization so I’m going to stick withyou for now and can you talk a littlebit about um how or if your organizationis adapting your Recruitment andRetention strategies uh to align withthe evolving I’m going to put air quotesaround diversityTrends yeah I I think it’s such a greatquestion you you know um previousorganizations that I’ve been a part ofhave um I I think exemplified this ideaof agility and codified it within therecruiting process and so that’sthinking about you know the rate atwhich technology is changing today is sorapid right we all know that the rolesthat we are doing today may not look andalmost certainly will not look and bethe same jobs that a generation from nowfolks will occupy and so how do youthink about that from a overall likeWorkforce Development Talent Developmentperspective um I’ve seen organizationsdo a really good job of this of sayingyou know what the skills required todayare definitely not going to be what’srequired tomorrow so how do I futureprooof my Workforce let’s think abouthiring on something like potential let’sthink about hiring on something likegrowth mindset and so it’s reallyinteresting right to take whether that’syour company’s sort of cultural tenantsand values there sort of ways ofoperating and turning that into yourassessment process or taking like aconcept like growth mindset and sayingactually we’re going to take a big beton this like recognizing that who wehave today like those skills canoftentimes be learned right we haven’talways known how to uh build largelanguage models or do AI at the level ofsophistication that’s happening in 2024but someone had to learn that at somepoint right and so how do we think aboutkind of hiring folks who have anaptitude a willingness and an interestin continuing to build net new skillsets um can be something that that’sreally powerful and I think also startstorethink um what are some of thosesystemic issues that may create gaps inwho has skills today well if we take ifwe say Beyond those skills that someonehas today what are they capable oflearning what are they capable ofacquiring that that can create a reallypowerful shift so I think that likegrowth mindset as a key mental model isis one I think the second piece that Iwould think about that um I think isemerging and you know particularlyrelevant as the conversation arounddiversity equity and inclusion ischanging in the US and then alsoglobally is this idea of like culturalcompetence as a required leadershipskill set so the way I like to think ofmy work and in my position is Amazon isa global company that happens to beheadquartered in the US and so what doesthat mean that means that culturalcompetence if if we believe thatcultural competence is required becausewe know that our teams are going to bediverse they’re going to come from allwalks of life they’re going to come fromevery corner of the globe they may speakdifferent languages they may havedifferentabilities our responsibility is toenable our leaders to be able to leadand unlock the power of that Workforceto unlock that potential and thatrequires cultural competence thatrequires sort of inclusive leadership asa core soft skill set um and so I thinkthat’s something that we’re we’recontinuing to see as as a trend is howdo we help enable people managers andand more broadly our leaders to havethat cultural competence because we knowthat the the teams that we build aregoing to continue to be more and morediverse but we won’t actually see thebenefits of that diversity if ourleaders are not equipped with the skillsto effectively lead those diverse groupsI love everything you just said um oneI’m a huge fan of growth mindset um andactually looking at the potential of thecandidates uh and not just what’s onthat piece of paper so um I would loveto dive into that further and then wheremy mind went as you were talking aboutcultural competency what does thattraining look like for your managers Imean we’re not talking about yourtraditional cultural bias training sowhat does that look likeyeah it’s a great question and it’ssomething we’re very much like have agrowth mindset around and and are tryingto figure out right I think it’sthinking about for example in particularlocations our operations managers may bemanaging teams of hundreds of people andthey may be fresh out of school rightlike they they may be early in theircareers themselves and managing teamsthat that come from very different walksof life Etc and so and it may representfor example different languages thanthey speak themselves and so I thinkthere’s a blend of like the culturalcompetence and like putting that onus onleaders to build skills of how do Iengage differently how do I buildempathy how do I understand and workwith someone who’s comes from adifferent uh background for me andcommunicates differently and hasdifferent values and differentpriorities on what they want from worklike that that’s one piece of it andthen there’s another piece of it that’slike how do we embed that in our systemsright so when it com comes to languageaccess there’s a lot more that we as anemployers should be doing to ensure thatat the site level some of that languageaccessibility is built in right so howdo we create trainings in multiplelanguages as as a basic you know exampleof that um how do we create access toEnglish language classes if that isgoing to be you know the that ispredominantly the language in which wedo business so how do we also tackle itfrom the sort of more systemic and umemployer side of it I I think it has tobe two way so that we’re not puttingeverything on our leaders but definitelyum open to ideas on like how we reallyland a cultural competence model um butI think it is a blend of of a lot ofdifferent pieces awesome so for thoseout there that’re doing coalitions whereorganizations come together I thinkthat’s something we can come together onhint hint hint um all right allright talking about Trends Leila I wantto ask you how do you see the insightthat represented helping your recruitingorganization uh yeah so um I appreciatethis question so I think uh if I couldsum it up in one word it would be umintentionality right I think when wehave an awareness of um what is likelyto occur when we’re not intentional thenwe have an opportunity to make consciouschoices about if we do want to changethese things where do we where can webecome more intentional right so I thinkthat’s probably the first thing from anawareness building standpoint and thenwhen I think about it from theperspective of like how we might actionyou know data like this or leverage thisas a directionally accurate um let’scall it uh Baseline right to inform ourImpressions opinions and and our our umstrategies it’s really aboutaligning uh our actions with theoutcomes right that we want to see umand I caution us all really to bethoughtful about not confusing activityor movement with progress right which Ithink is what we see quite a bit um andso we have to also have the disciplineand the I’m going to call it theintestinal fortitude right to stick withwhat is a multi-year play right this isnot going to change overnight we can’tjust do one partnership with one ofthese enrichment programs and think heywe’ve got it figured out it really issustainconsistent discipline and I would say tothe degree that you can um leverage dataand analytics to make it rigorous umit’s really about being intentional andthoughtful about how are we going tomove the needle right and so I couldtalk until I’m BW in the face aroundwhat that looks like and what um I meanand also I should say transparently thatI I am the poster child for theseenrichment programs for the schools thathad you know the mentors I’ve got amentor who have been talking to since Iwas 15 for my first corporate gig andeverything from like don’t show up latehere’s what happens right to um here ishow you set yourself up to to to to um Iwould say to mitigate the impacts ofthat Network Gap that you talked aboutearlier Maya it’s participate in thisprogram where there’s another 250students that look like you and have asimilar background to you who are alsotrying to break in right so surroundyourself with a community of people thatyou can race with and so um but thatdidn’t happen in just like that that’sthat’s like a year not not a year sorrythat’s like a a lifetime of continuedinvestment in in me and in the programsin in the institutions and it’s reallythese it’s really companies like ourssaying we’re in it for the long haulright it’s it’s really us saying werecognize that sometimes um there’s atension between speed right and and andthe quality right of the hires that wemight make and saying you know whatwe’re going to have to slow down becausewe want to maintain the Integrity of ourprocess and by Integrity I meanincluding when we think about um itbeing inclusive and diverse and beingrepresentative of all the all the needsand um all the needs and requirements ofthe different candidates that we want toattract so um I will pause there becauseI feel really passionate about aboutthis um and but I mean I’d love to hearwhat other folks have to say I feelpassion coming through what what reallyspeaks to me is basically you’re sayingyou got the cheat code through yourmentors and your sponsors or those thatthat in other words we’ll say you gotthe the hidden things that are publicand known to others um so what are thetips and tricks that we need to to movethrough our networks and up through theladder if we can say it like that umthank you for that so I want to let methrow this out to all of you um as weare still on the theme of Trends what orif any or are you using are there anyemerging Technologies ormethodologies that could help improvediversity in the tech Workforce that youcanshare I feel like this was a hardone I I can share a little bit I thinkyou know um I sit in the recruitingspace so I’m more familiar withtechnology that that supports our hiringprocess and at massive scale sincethat’s the the scale that we’reoperating on um so for some roles we usemachine learning actually to helpidentify candidates that should moveforward in our hiring process and likeand essentially um be able to to moveforward without having to wait for aresume review by a recruiter um and soall of those candidates are ultimatelyreferred back to our recruiting team forreview and considered for next steps butit’s really based on that candidate’slikelihood to besuccessful and you know I think anytimewe talk about algorithms Etc there’slike all these questions that come uplike what was it trained on is it fairEtc and what I think is really uniqueabout some of the recruiting technologythat we’ve built is that the premise washow do we build this to integrate morefairness in our hiring process and sowhat we’ve actually seen is that whenthat tool is used um our data shows thatthe the tool actually like increases thenumber of applicants who reach the nextstep in the interview process that arediverse and it does so in a really fairway and so the key takeaway there for meis you know beyond sort of you knowunfortunately this isn’t like atechnology that’s available to others soI can’t offer sort of a name to go to goby but I think what’s key in that lessonlearned is that Amazon is reallypromoting this idea of having borninclusive technology meaning you Centerfairness you Center Equity at theproduct development like the early earlystages rather than sort of anafterthought of a like bolted onsolution later and so what that meansfor us is saying like that’s going to bea core tenant that we go and build on islike we want to create fairness in theprocess and so we’re going to becontinuously inspecting the outcomesbased on demographics and understand whois this working for is it working forall is is it actually helping toincrease fairness in our process yes orno we’ve found that you know we are ableto to increase fairness and increasediversity as a result of tools like thisum but I think it’s a really importantkind of operating model for us too thisidea of like being born inclusive andthinking about technology as potentiallybeing a force for increasing Equityincreasing inclusion but I I think forme the thing that stands out is likeunderstand the Genesis of some of thistechnology was rooted in a thesisstatement of like how do we ensure afair process and like that to me I thinkenabled the outcomes that we’re able tosee because it wasn’t a oh as anafterthought let’s see if this isworking for everyone it was a actuallylet’s make sure we’re doing this rightfrom theoutset thank you um we see yourquestions that are in the Q&A pleasecontinue to throw your questions inthere we willweave them in but I want to bring Josephinto the conversation um we’re going tojump to measurement andaccountability soJoseph how do you recommend companiestrack progress towards diversity goalsI’m gonna throw the softball out thereand then we’ll dive deeper yeah for sureum I think the when I think about thatquestion I think there have four waysthat that could be done you four foursteps I would say I think the firstfirst thing is starting with a with agoal like that is not just oh it’s it’sa goal but it’s a very clear measurablegoal right are we and this goalshouldn’tbe an easy goal let’s say you’re addingyour your representation of for womenyour company every year grow Grows by 1%don’t not don’t come say okay we’regoing to have a goal of growing therepresentation by 1.2% this year that’sthat’s not a that’s not a goal that’sthat that pushes so a goal to improvediversity has to be to some degree hasto be uncomfortable now it shouldn’t beoutrageous at the same time because ifyou’re looking at the market and you’resaying okay well we’re grow by 1% butyou come and say look this year we’regoing to grow by 50% now that’s also notgoing to work and so it has to be athing that based on the data that youwere seeing and based on the goals thatyou were trying to achieve as anorganization um this is what we need toset so it has to be a clear goal and ithas to be measurable that’s the firstthing and the second thing is you haveto utilize the kpi so so what are wegoing to measure here are we measuringthe thesatisfaction within organization of thedifferent you know groups are wemeasuring the promotion rates is arepresentation is a retention what arethose metrics we’re trying to and in myopinion I I usually advise organizationsthat we work with is not to set too manybecause a lot of times you’re going tosee different things different issues inthe organization but if you said ohwe’re going to do like representationwe’re going to do you retentionpromotion at the same time A lot oftimes it becomes and so you might so mymy thinking here is you set one or twoof those goals hey we want to increaserepresentation by this much in the next18 months in the next we want toincrease promotions right so you set acouple of those goals the third um andhave thosekpis the third thing is have theaccountability and transparency I seeoftentimes organizations say okay we’regoing to we have these goals this iswhat we want to this is what we want toaccomplish however head of Dei it’s yourjobgood luck witheverything that doesn’t ever work youhave to bring in all the leadership andengage them so you were ahead of techwell in your or CTO you have 90% menthat is a problem okay what are we goingto do here so you have to involve theleaders in there and I know this soundssometimes people like oh my gosh wellyou have to tie it to Dollars you haveto tie the progress to Dollars you talkabout like the the bonuses that they getfor accomplishing XYZ well yes theum uh goals should be part of that bonusas well and then you start to see thingsgetting done very quickly because no onewants to lose money right and then thefinal thing I would say is is importantto celebrate those wins right so if youhit setting goals and and and is an EverChanging is a never ending process ifyou hit those goals it’s important tocall them out celebrate those thingthose wins and then keep going again soI think a continuous process but that’sthe way sort of I think about thattracking towards diversity goals toawesome thank you Leila used one of myfavorite words earlier to when she saidintentional um intentionality I think ishuge um especially as we’re talkingabout moving any needle forwardso using data to first of all understanda baseline of where your organization isyou have to as I love to saydisaggregate the data by a minimum ofrace and gender um that’s diversity 1.0and then 2.0 you got to add those otherfact on that we care about being sexualidentity do you care about caregivers doyou care about veterans we can go on andon and on um and and so Joseph I’m goingto stick with uh you on this nextquestion what metrics should companiesconsider when evaluating their successof their diversityinitiatives yeah not for sure I think itdepends I I know it’s an answer like itreally depends I think it depends on themetrics and the goals that you set tobegin with depends on the goals that youset because eachorganization have different pain pointsnow I’ve seen some organizations thatwhen it comes to the break the the thethe representation of women and menthey’re on pity and that’s wow that’sfantastic but when you now look at it interms of let’s say promotions intoleadership level that becomes the issueand not the overall representation andfor some other organization it’s a wholedifferent thing so I think it depends soI think it’s looking at the core thecore things right when you think aboutthe the employee life cycle they arecore things that that playe so you talkabout the sourcing that’s the hiring whois coming in top of the funnel rightwhat does that look like the hiring youtalk about the representation you talkabout promotion you talk about the theemployee experience and you talk aboutretention SL exit so the these arecategories along the life cycle and soyou have to pinpoint where which areashave the most issues where are the painpoints and then focus on that and ifit’s a thing for you saying Hey listenwe are actually good at getting you knowfor example women into leadership roleshowever our problem is top of the funnelthen the metrics that you should belooking at should be your top of thefunnel metrics and then you should beworking towards how do we even write ourJDS to even begin with like what aboutdo we have a diverse candidates latebecause I’ll tell you right now ifyou’re going into a company and you’retrying to interview and every the 10people you interview with all white forexample and you’re a black person youwhoa what’s happening here so these areso you have to you know who what’sdivers Candis look like what’s our JDlook like is it inclusive so these arethe questions you ask yourself so Ithink it depends on the pain points thatyou first of all have to pinpoint andthen the metric is going to just comeinto play based off of that so that’s toanswer your question it depends I knewthe answer depended but I wanted to seehow you phrased it thank you Joseph forthat um I want to quickly touch onglobal perspectives uh so what I know Ihear some of our audience members sayingit’s not just about the United States Iknow that we represent Globalorganizations and global companies soMaya let me throw this at you are thereRegional or cultural differences in thechallenges or strategies fordiversifying the tech Workforce thatyour organization is focusedon yeah absolutely I I think you know wetry to operate with this mental model oflike think global act local right sorecognizing that like we absolutely needa sort of global diversity equity andinclusion strategy that to some extentis representative of our organization’svalues and the specific ways that thatmanifests the specific issues ofspecific communities that aremarginalized are going to varysignificantly based on geographiclocation right and so you know some ofthe examples that I think of are youknow thinking about our indigenousstrategy in in Australia is going tolook really different from thinkingabout our Native American strategy inthe United States um thinking about raceand ethnicity in the United States is avery different conversation and thematurity around that conversation ismuch more uh sort of advanced than inthe UK where that’s more of an emergingissue and and and topic and Trend um youknow in some countries there areregulatory requirements or or pushes forhiring people with disabilities andmeasuring our commitments to that spacein particular um and so that createssort of different levels of okay is thiswhere our focus should be if we’re goingto be reporting on it and it has to alsobe we also have to consider sort ofcultural stigma within that as wellright so within within a particularculture is there more aptitude forsomeone toself-disclose as having a disabilityabsolutely like we see that varianceacross the globe and so you knowultimately we we’re really going to makeprogress on the things that we’remeasuring and I think we have to takeinto consideration a number of factorswhen we ask someone to share theiridentity with us as an employer um andso you know I think about too as itrelates to sexual orientation and andJoseph had some some caveats right to tothe data that is in the ACT report oflike okay it’s only binary we’ve gotthis sort of other category it’s verysmall Etc I mean I think we have tothink about that also on a macro levelof in some countries there is stilllegal lications for disclosing yoursexual orientation and so you absolutelydon’t want to be in a position to saywell globally Amazon’s going to collectthis data if that’s going to actuallyput potential employees or candidates atrisk like we’re we’re not going to dothat so I think taking intoconsideration like it it’s the thecultural attitudes it’s also like thelegal requirements we’re absolutelygoing to dock into like what isacceptable like legally in in eachcountry in which we operate um but italso really helps us think about how weneed to um Advance the Dei conversationin a very Market specific way right andand really consider who is sort ofexcluded from Economic Opportunity inthis particular community in which we’reoperating how do we unblock some ofthose particular barriers to entry ifthey’re more pronounced for for some umand versus others awesome thank youmayamLeila I would love for you to share morewith the audience if if and how you feelthat lessons from diversity initiativesin other Industries can be applied tothe tech sector um I think okay so firstI should say that I absolutely dobelieve that lessons from otherIndustries can be applied to the techsector I’ve had the great Fortune ofworking across a number of differentIndustries and I’m literally justcollecting different tactics and tricksum and putting them in my toolbox um soI would say um just to be a little bitmore concrete and take it more um fromthe abstract to more actionable thingsthat people can think about when I thinkaround for example um when I was atdanaher it’s a very like danaher startedfrom like a tools business right with ummanufacturing floor and measuring thingslike cycle time and um these veryoperations um oriented metrics uh when Ithink around how we take what ispossible in a manufacturing floor andsystematize and develop structuresaround how we can measure progress forthings that might seem that they’re soabstract that we can’t measure progressbut we can use inputs as proxies to anextent right that’s an application ofsomething that you would see in amanufacturing floor that could benefitthings like how we look at reporting andanalytics from a recruiting standpointright um where the output really and theand the product that we are reallycreating is delighting and employee likeour our employer like our employers withtalent that would um that would reallychange the game for their companies andthen I think about other places like ummy exposure to like consulting firms andor um other places where you have toreally be thoughtful about problemdefinition and structuring problemsolving in a way that causes people tolike converge and diverge so that youhave a robust set of that you can thinkabout um and that you can Implementright and then I think also about likeplaces like um um where it’s a morecreative environment where we leveragethe benefit of like experimenting andfailing fast right and really thinkingabout the interventions that we mightput in place around career progressionor succession planning thinkingcreatively around how we can trysomething so that we can learn somethingand then iterate so that we can getbetter as opposed to like we tried it itfailed let’s never do it again right sothis um experimental mindset around howwe design interventions to to make tomove the needle is another sort of Iwould say like very tactical applicationthat is something that I’m borrowingfrom elsewhere and bringing with mereally everywhere that I go and I I’llbe the first to say that not everyenvironment is open to these things Ithink you have to really be thoughtfulabout the context the environment um thethe existing norms and culture in theenvironments that you’re in in order forthis to to sort of to land or to bebeneficial and so it’s not um it’s not asilver bullet but I I see I see thoseconnections and as someone who’s more oflike a systems thinker um and who haslike an associative sort of like mind II can even go as far as to say that Isee really interesting parallels betweenthe sales customer acquisition processand the talentacquisition process right and that likewhat if we partnered with our salesorganization to figure out hey how do weum how do we structure the things thatwork for you and see if they might alsobenefit us in the talent acquisitionprocess and then the last piece thatI’ll mentioned relating similarly tothat is when we think about um when wethink about uh creating those um thoseexperiences for different uh fordifferent um candidates thinking aboutlike how we do customer Journey maps inproduct right and and really centeringthe needs of the candidates right isanother like really sort of like copyand paste it from a different part ofthe organization and see what you learnright I don’t think we need to figurethem out in the talent space necessarilyI think the business is Rich with umwith um other strategies that we couldImplement if we would just be willing toswitch if we would be willing to justswitch out our lenses a little bit andsee and see the possibilities as opposedto like the reasons why that wouldn’twork I love that I have more questionsbut we I’m looking at the time and Iwant to get to all the many questionsthat the audience has and so I know howto reach all of you and I’ll ask myquestions to y’all offline later so realquick lightning round as I tee up theaudience questions real quick y’all realquick lightning round what is the oneactionable takeaway you want theaudience to learn from today’s webinarand I’m going to start with Joseph realquick it’s a good I been thinking aboutthis um I think I think that I thinkit’s I think it’s starting with whereyou are um I think a lot of and I saythat because we’ve seen a lot oforganizations say oh but we don’t wantto expose this data because we don’t youknow we don’t know what’s gonna happenbut change is never going to happen ifyou don’t start with where you are andit doesn’t have to be a big thing youcan start with onespecific team one specific problem andthen go from there but if you never tryand understand what’s happening withwithin your organization change is nevergoing to happen and we’re going to betalking about the same thing 10 yearsfrom now right and so I think it’sstarting with where you are little bylittle and it’s a process and that’s onething I want I want everyone to takeaway from here as well so yeah thanks onthanks Jess Lea I see you haveMike yeah so I um I’ll be brief I’ll sayI’ll start and but I I’ll caveat it bysaying not caveat but I will preface itby saying that the truth shall set usfree right so problems are so mucheasier to solve when you actually knowwhat the problem is so if we want tomake progress right this idea of problemidentify like identifying these problemsor these opportunities is really what isgoing to change the needle and if youknow data is what we need ordirectionally accurate Baseline is whatis helpful in terms of moving thatconversation forward then certainly leaninto that but like let’s not getdistracted having a conversation aboutwhat we don’t have and really focus onlike how can we be intentionalintentional with what we do have andstart to make progress with you knowwhat’s available to us we don’t have towait absolutely thank youMaya yeah I think mine would just bediversity equity and inclusion work isnot hr’s job not Talent acquisition jobit is every single person in theorganization’s responsibility and so ifyou drive accountability at that stageof saying everyone has something tocontribute to this whether it’s showingup as a culturally competent leaderwhether it’s making space for yourindividual employees accommodationswhether it’s being a thoughtful teammateand demonstrating inclusion that way orwhether it is sort of higher orderlevels of of decision making um I thinkwe absolutely have to take an integratedapproach to making progress on diversityequity and inclusion and that meansmaking it everyone’s individualaccountability awesome okay I’m going todive into a couple of these questions umwe talked earlier about networks and myquestion is for you but anybody canchime in um Can the panel speak aboutany impacts they’ve seen with emergingTech maybe AI um and its impact ondiversifying theworkforce yeah it’s it’s a good questionum I I think there’s a ton ofopportunity here and and I want to beclear that I’m by no means an expert inthe AI space um but but I think it isthis idea that that I started toreference which is like how do you thinkabout being born inclusive and centeringequity and inclusion at the outset andthen in every subsequent phase of ofproduct development and so if you arebuilding AI technology or or any Techreally it’s really thinking about thisidea that like that Tech should work fora really diverse customer base and inorder to do that you need to havediversity as well in your productdevelopment phase so who’s in the roommaking that decision on what’s on theproduct road map who’s informing thatwho’s testing it and then whatinspection points do you have foryourself along the way right so are youdoing a rigorous testing ofhey here is a a talent product is thisreally being looked at from the lens offor every identity group are we seeingthe outcomes that we need to be seeingand so I think that like inspectionpiece is often times after the factright so like once it’s gone to Marketonce it’s really big once it’s you knowgot got a flashy headline behind it andand and tons and tons of adoption andthat’s oftentimes too late so thinkingabout um really this idea of like beingborn inclusive from the outset is wouldwould be my my thought and andguidance absolutely thank youMaya Joseph the next question is for youthere seems to be inherent bias in theapplication process how do employersbetter prepare diverse candidates betterprepare candidates to com complete theprocess that puts them at a disadvantageat the onset and are there better waysis to root our biased in Talent machinelearning yeah I mean I think I think itstarts with so there’s two parts of itright so there’s there’s the productpart that people buy and then there’swithin organization how you implement MLand we you know the question also inthere’s a lot in the question but italso includes the the training umhowever I think looking at the productand the decisions organizations makefirst of all as a someone that buildsproduct organizations that build productbasically you have to do no harm um andpart of that is the whole concept whenit comes to data science called test andlearn so if you’re building a productand you you write an algorithm it’simportant to test those those the algorthe outcomes and learn and then redo itand that’s a whole iterative processwhat is more important is the peoplethat are building this these systems andthese algorithms it’s important that youhave a diverse um uhproduct analytics engineering slate aswell so the people that building thismetrics these these these algorithmsit’s important that you have diversitypeople thought because that would alsohelp in terms of how you test and howyou train your models on the other handas anorganization it’s important not just toimplement the models but also haveideologies in place as well so sayingokay yes we’re going to have a thismachine learning model help us in termsof this highend process however we’regoing to have a diverse candidate slatehowever we’re going to test and makesure that like it’s not we don’t it’sit’s not veering towards a particulargroup of people right so I think it’simportant to keep testing and keeplearning along the way as well um andthen of course um I think in doing sothat you you know you remove this wholethis whole outcome that could happenwhere oh we we we used an going to hirepeople however oh my gosh like out ofall 100 women that applied 80 89% ofthem were were not even moveed to thenext stage right you start to remove allof those things as well so I would sayit just comes back to product ideologyproduct equity which again the techaccountability Coalition is working witha whole group about that as well butproduct Equity is very very importantwhen we start to think about ML and AIin thisspace aesome thank youJoseph Leila the next questions for youyou um you talked a little bit earlieras I dubbed the cheat code um so do youhave suggestions for organization sitesor people to help bridge the networkingGap um I I I have I have many thoughtsI’ll be brief um so uh I will say thatthere are it just depends on where umfolks want to um invest right but thereare organizations at the college accesslevel like bottom line for instance helppeople get into college and to choosecareers like this then there areorganizations like MLT for examplemanagement leadership for tomorrow thathelp them to get FastTrack jobs andaccelerate their careers and help themyou know literally change the trajectoryfor their families in some cases andthen similarly MLT also plays at the NBAcareer level there’s um um there aresponsors for Educational Opportunity SEOuh and then there are programs like Prolike um for example high-tech is one intech for Hispanic folks or Latinos Imean I could go on there’s ICM um itmfright um there’s so many there are somany organizations I would say that oneum one caution I have for folks that arelooking into this is don’t underestimatethe amount of like this is like this isthe bread and butter of theseinstitutions these organizations andthey have a formula that works they havedone this for a while sometimes we thinkoh I can throw together a mentoringprogram or I can throw together a careerdevelopment program and I would say thatwe are better served um allowing theseorganizations that are like experts atwhat they what they do build thesethings for us in ways that are authenticto our environments because they’ve gotthe proven The Proven formulas right andso invest in these organizations theseenrichment programs that are helping Iwould say to to address the broadercontext that Tech the companiesthemselves or ourselves we can’t reallyaddress um and if folks you know want tohear more I’m I’m available on LinkedInand happy to to send links and websitesyour way um or to just have a actualconversation about these things butthey’re a ton out there y’all th foreveryone you I’m I’m like yes yes yes toeverything you just said now be remissedif I don’t say reboot representationy’all re rebootrepresentation so for those that don’tknow um we I already have the privilegeof partnering with these amazing peopleon the screen but there’s we have manyother members and partners corporatemembers and partners that are supportingum several of the organizations Leamentioned so if you’re students um youshould know about organizations likeship color stack code paath Last Mileeducation fund um rewriting the code andthere’s many many many more um go to thereboots website and you’ll see some ofour grantees there U but there’s noshortage of programs that um we can findfor networking incommunity okay okay more questions soI’ll throw this out for anyone who wantsto answer how can I get my employer andor my executive colleagues to be lessfearful of collecting self ID data andactually using that data to makedecisions for the company’s Deiwork Joseph I feel like that has you allwritten all all over it but Lila you’realso off Mike I think I think oh yeah Ithink Lea my as Executives at largercompanies I’m sure they have dealt withsome of this sort of questions as well Imean I I I I try to encourage them but Ithink I think they’ll be better off I Ilove to hear their perspective onthis um I think my my initial thoughtsare[Music]um to flip the question and to say topoint out um to point out the cost ofnot doing this right to point out thecost of um not being POS not positioningyourself as like an inclusive placewhere people would want to work topotentially miss an opportunity to alignyourself with talent that I mean when wethink about the generations that aregoinging into the workforce now they’rejust not having they’re not having itright this is a requirement and so froma talent strategy standpoint from aninnovation St standpoint there is a costto not invest to not investing in havingthis data and even a great and evengreater cost to um I’m G to say um towalking in the dark right and just throwthrowing things at the wall to see whatsticks as it relates to this becausemore than anything you don’t want tocompromisethe trust of your employees and of yourcustomers and the more you are doingthings that seem I would sayasynchronous with um with what is reallygoing on at your company it’s only amatter of time before it gets out andwhen it does it’s going to be hard to tobuild that trust back so I think goingat it the other way around from likewhat there is potentially to lose um canbe compelling wherever wherever umwherever doing the right thing isn’tcompellingis a great is a greatmotivator that part I agree with youLeila I I think that’s um that that canbe a really critical motivator and Iwould just add to your point on trustthat it’s trust with external folks thatyou want to attract to your organizationand then it’s also the expectation ofyour current Workforce right and so inthe absence of data anecdotes are goingto tell the story of the employeeexperience and so if you don’t actuallyyou know as an executive team if youdon’t have a good handle on not onlywhat does representation look like buthow are employees progressing throughoutthe organization who’s sticking aroundwho’s getting the the big jobs who’swho’s um really being able to impact thefuture trajectory of theorganization in the absence of havingdata to be able to tell that story oneperson’s you know nightmare experienceis going to Define your organization soif you want play into the fear thing Ithink that’s like a very real thing thatwe have seen play out um with with otherorganizations awesome all right I thinkthis might be the lastquestion with hiring backgroundquestions are hiring backgound questionsare standard does anyone have feedbackon how to eliminate bias on this processand building need to know Equitablebackground data a collection that won’timpact strongcandidates I think that goes along withtrust yeah I mean I think there’s a aton here I can take a stab at I’m notsure if I’m directly answering thequestion but one piece of this to besort of thinking about this and if IChannel um some of our my legal teampartners right there’s a piece of thisis like employment law and making surethat we’re we’re doing right by fromlike that perspective there’s anotherit’s like Privacy Law right and thinkingabout how we Safeguard informationthat’s being captured so for example ifyou are struggling with even drivingadoption of self-id data part of theconsiderations from the outset need tobe well like who will have access tothat data in what ways can it be usedwho is need to know how do we ensureit’s anonymized and aggregated andreally being protected so that we don’trupture trust with either candidates oremployees and so that that’s somethingto maybe just be be thinking about isand I think also to some in some ways tocommunicate um where where appropriateto your Workforce right H H why are youcollecting this why does it matter howwill it be used um and and it comes backto trust because you will see lowerself-identification rates if yourWorkforce or candidate pool does notbelieve that you will use that data withintegrityMaya Leila Joseph y’all are amazingthank you so much for your expertise andyour time and your willingness to shareyour your insights to this amazing panelthank you for thediscussion the fact that we still haveso many attendees engaged askingquestions along the way is a testamentto the collective Brilliance of the fourof you so on behalf of the Coalition andthe as Institute thank you for your timeenergy and just knowledge sharing we’vegotten lots of sort of um emails andside chats already saying that this waswhat people needed to hear they need thetools they need the practicality theyneed to know that they’re not alone indoing this work and the collectivewisdom that was shared was incrediblyimpactful so thank you four for joiningus and to folks who’ve been tuning inthis whole time there some of theinsights that we heard over the lastlike 45 50 minutes was about emergingTrends and cultural competence andgrowth mindset which I talk to my likeeight and four-year-old about all thetime right growth mindset and that theonus is on us to do this work not justthe folks who would tune into to a datawebinar about demographic Trends and Deiin the middle of the day because likewe’re all kind of geeky in that way buthow do we translate this back to ourpartners um in the work to folks who arelike this is not my job I don’t have anyinterest in this but actually havetremendous opportunity Authoritysociopolitical Capital leadershipCapital to make these decisions I lovethe idea of data informeddecision-making around this and we knowthat we can only have as good data andas I said at the top of this as we havethe opportunity to ask the questions Ithink Leila you specifically talkedabout you know we can’t answer problemsthat we don’t know exist and so weactually have to kind of look ourselvesin the mirror and be like what are theproblems we’re trying to solve here andthen we can start the change Jo if youtalked about um I think both of youtalked about change isn’t going tohappen if you don’t start right and thetruth will set us free I reallyappreciated the answers on how youconvince colleagues who are a little bitnervous to get that data out there assomeone who’s worked in Tech as someonewho works in this space now someone’sgot to push a little bit right and ifwe’re doing it together we get evenfurther the thing I want folks to sortof leave with after this is we talkedabout cheat codes rightI think all of us have had either cheatcodes we figured out someone’s given itto us or at least I know confidently noweveryone here shares those cheat codesout right what I want you to know isthat we the Coalition have the cheapcodes too it’s literally by bringingfolks together in this way I really wantto encourage folks to think about howyou want to shape the way the techsector is evaluating this work add yourmark be challenged by the findingsthat’s really important a lot of folkscome in including people with the rightintentions around the guy with one viewon how it should be done and refuse tobe humbled by what the information tellsus I do this work because I can remainhumbled by it all the time I’m learningall the time by it um even in preppingfor this webinar the learnings I hadfrom my sort of co-panelist I was likeoh wow and we like took that back and sowhat I want you all to think about ishow to be challenged by the findingshumbled by the learnings and inspired togo further together thank you all somuch for tuning in again hope you outthe feedback survey that we dropped inthe chat but we will be following up ifyou have any additional questions weknow there were some questions wecouldn’t get to please let us know we’lldo our best to follow up and onequestion that came up repeatedly is whenwill we see more of this data we will bepublishing some additional toplines andinsights um over the next couple ofweeks but the more you ask the more wecan share as I mentioned we have over300 different data permutations and wewant to make sure that we’re offeringthe most exciting contemporaneous andinsightful insights to everybody have awonderful afternoon and I look forwardto how we can can continue to buildtogether thankseveryone
The Tech Accountability Coalition has been collecting a unique and cutting-edge dataset of tech workforce insights over the past two years. Working with analytics firm eqtble to source, aggregate, and analyze workforce data from almost 50 US-based tech companies, we are thrilled to share our insights.
In this session, hear from the Coalition team and eqtble, as well as from an engaging panel of tech equity and workforce leaders discussing the challenges, emerging trends, and actionable strategies that are relevant to the work of tech sector leaders committed to principles of diversity, equity, and inclusion.
Speakers
Maya Appiah Global Head of DEI, Amazon Stores Talent Acquisition, Amazon
Read about Maya
Maya Appiah heads up the global DEI function for Worldwide Amazon Stores Talent Acquisition. Amazon’s aspirations are to become Earth’s Best Employer, and my team’s efforts have a direct impact, accelerating this journey. Our recruiting team hires the largest breadth and volume of positions across the field, corporate and tech roles at the company.
Maya leads a talented team of program managers and analysts who work to equip our recruiting teams with DEI data and talent intelligence and upskill them with learning & development opportunities and career pathways to truly be agents of change for DEI at Amazon. We are focused on earning trust with historically overlooked groups to support our talent attraction initiatives through employer branding and events and making long-term investments in the communities in which we operate. We inspect our candidate experience and recruiting processes to drive more inclusive hiring practices for people with disabilities, and refugees, recognizing that our focus on these groups will yield a better experience all up.
Dwana Franklin-Davis is the CEO of Reboot Representation. She is a collaborative and compelling visionary leading the Tech Coalition’s pooled philanthropic investments that enable Black, Latina, and Native American women to graduate with computing degrees by 2025 and lessens the diversity gap in tech.
A lifelong technologist with a passion for increasing diversity, equity, and inclusion in the tech sector, Dwana joined Reboot Representation in 2019 after working in IT, software engineering, and leadership positions for Mastercard, May Department Store Companies, and IBM. Based in New York City, Dwana holds a BS in Management from Purdue University, an MS in Information Management from Washington University in St. Louis, and a Certificate in Project Management from Washington University in St. Louis.
Joseph Ifiegbu is the Co-Founder and CEO of eqtble. Joseph was previously the Director of People Analytics & HR Technology at Snap Inc. from January 2020 to December 2021. Prior to that, they were a Member of the Board of Advisors at DataVLT from January 2017 to December 2018, where they focused on blockchain and analytics. From January 2017 to December 2020, they were also the Director of People Analytics at WeWork, where they built the People Analytics Team and implemented the infrastructures & analytical processes for optimization and scalability. Joseph has also held positions as Lead Data Scientist at Toys”R”Us from January 2015 to December 2017, where they led a team that created a space optimization model that helped generate additional $3MM in revenue in Brick and Mortar stores, and as a Data Scientist at Arsenal F.C. from January 2013 to December 2015. Joseph began their career as a Researcher at Scientific Research (SR1) from January 2012 to December 2013.
Joseph Ifiegbu completed a Master’s degree in Statistics from Bentley University – McCallum Graduate School of Business, a Bachelor’s degree in Electronic Communication and Mathematics from Belhaven University, and a Certificate in Cybersecurity from Harvard University.
Ms. Layla A. Ramirez is the Director of Diversity, Equity, Inclusion & Belonging (DEIB) at Justworks, a company on a mission to help entrepreneurs and businesses grow with confidence. She is responsible for operationalizing, integrating and continuously improving Justworks DEIB strategy throughout its workforce, workplace, and marketplace.
Layla joined Justworks, after having held DEIB positions at Amazon Web Services, Netflix, Danaher Corporation and Management Leadership for Tomorrow (MLT). Prior to launching her career as a DEIB practitioner, Layla spent several years as an Analyst in the Global Wealth & Investment Management division at Merrill Lynch.
Layla holds a B.A. degree in Economics from Smith College and an MBA from Harvard Business School. She is passionate about leadership, DEIB, college access, coaching and ultimately enabling others to be the best version of themselves.
Layla is based out of Brooklyn, NY, where she lives with her partner.
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