O slideshow foi denunciado.
Utilizamos seu perfil e dados de atividades no LinkedIn para personalizar e exibir anúncios mais relevantes. Altere suas preferências de anúncios quando desejar.

Ep 184 - Employment Success for People with Disabilities

19 visualizações

Publicada em

Today, I’m joined by Jim Fruchterman. Jim Fruchterman is the founder of Benetech, a non-profit that empowers communities with software for social good uniting two worlds: the social sector and Silicon Valley. They work closely with both communities to identify needs and software solutions that can drive positive social change.

Jim is a former rocket engineer who also founded two successful for-profit high technology companies and has received numerous awards, including the MacArthur Fellowship and the Skoll Award for Social Entrepreneurship, in recognition of his work. He is a Distinguished Alumnus of the California Institute of Technology (Caltech).

Publicada em: Recrutamento e RH
  • Seja o primeiro a comentar

  • Seja a primeira pessoa a gostar disto

Ep 184 - Employment Success for People with Disabilities

  1. 1.   Episode 184 : The Future of Work:  Employment Success for People with  Disabilities  Episode Link: <Insert when published>     Intro: ​[00:00:00] Welcome to the workology podcast a podcast for the disruptive workplace leader. Join host Jessica Miller Merrill founder of workology dot.com as she sits down and guest to the bottom of trends tools and case studies for the business leader H.R. and recruiting professional who is tired of the status quo. Now here's Jessica with this episode of workology. Jessica: ​[00:00:25] We're talking about how to expand employment opportunities for people with disabilities. Technology that is accessible plays a big role in that and there is a great program in Silicon Valley that is leading the way driving discussion and conversation among some of the biggest brightest and most interesting technology providers and how to make the employment process more successful with technology. This podcast is sponsored by clear company and this particular episode is part of our future at Work series in partnership with Pete. The partnership unemployment and accessible technology. Today I'm joined by Jim Fructerman Jim Jim Fructerman is the founder of Bennett tech a nonprofit that empowers communities with software for social good. Uniting two worlds the social sector and Silicon Valley they were closely with those communities to identify needs and software solutions that can drive positive social change. Jim is a former rocket engineer who was also founded two successful for profit high technology companies and has received numerous awards including the MacArthur Fellowship and the School Award for Social Entrepreneurship in recognition for his work. he is a distinguished alumnus of California Institute for Technology or Caltech. Jim welcome to the work ology podcast. Jim: ​[00:01:40] Delighted to be here Jessica. Workology Podcast​ ​| www.workologypodcast.com | @workology
  2. 2. Jessica: ​[00:01:42] Talk to us a little bit about your background before you go I will say you're not the first rocket engineer that I've had on the podcast but I would love to hear about your journey to where you are now. Jim: ​[00:01:53] Ok. And of course I have a perfect record because my only rocket actually blew up on the launch pad. So I hadn't heard that successful at failing to launch a rocket at least more than the first top two thirds of it but so. So my background is so I'm a nerd. I went to Caltech because that's nerd Mecca became a engineer when just an effort the start of PDT program because I thought I'd be either a scientist or a you know or an astronaut. But there was this thing called Silicon Valley going on and so I took a leave of absence to join the first private enterprise rocket company and I was their electrical engineer. Unfortunately rocket blew up and I came back instead of restarting my PHC program. I started seven high tech companies in Silicon Valley and only five failed and the two that actually worked were both in the A.I. machine learning area where some of the early companies that area because we made a machine that could read anything and that had great commercial applications. But when I pitched our venture capital board on why put disabilities to our other wall I was expansive product line they vetoed the project a one million dollar your market just didn't make sense to them given their twenty five million dollar investment in our company. So as a result of that I started a nonprofit on the side to actually make reading machines for blind people. And that took over my life so I now spend almost all of my time in the nonprofit sector helping people who want to change the world for the better understand Silicon Valley understand what technology can do for them translate and when there's a big gap. Help set up a new company. I'll be at a nonprofit one that can help solve that problem. That may be Silicon Valley will never get around to because there's not a big market in human rights for helping disabled kids or helping environmentalists or whatever the social cause might be. Jessica: ​[00:03:56] One of the focuses for Benetech which is the non-profit that you've been talking about is helping people with disabilities find employment opportunities. Can you tell us more about Benetech and how you are partnering with forward thinking employers to build a strong business case for hiring people with disabilities. Workology Podcast​ ​| www.workologypodcast.com | @workology
  3. 3. Jim: ​[00:04:14] Sure. So is to be in this place between the social sector. And and the tech me Silicon Valley. And so our job is often to dig into problems that companies are facing. So I'm often in conversations with major tech companies when they get sued over some technology that people felt was discriminate against them. Of course I think our are our favorite sort of things to do with with the tech industry is how to help them build better products so that they don't get sued and their customers are delighted with the products they build. So that means we've worked a lot on accessibility accessibility as it affects education and accessibility as it affects employment. We've written tons of software products for people with disabilities. We've worked the publishing industry to make it easier for their e-books to work for people with disabilities. We've worked with the software application makers on how to make their technology better for people disabilities. So that's so in the case of disabilities we're sort of in Valley nerds who also understand the the variety of needs that are faced by people who are experiencing disability by kids that find themselves with different kinds of challenges or people as they age Ben attack published a significant report which we're going to link to in the transcript of this podcast. Jessica: ​[00:05:38] In November 2013 on the subject featuring a number of key learnings that you guys found around employment for people with disabilities and technologies can you kind of walk us through a few of those. Jim: ​[00:05:51] Sure. And essentially the challenge that was given to us by one of the largest donors in the disability fields of a wealthy family that has a family member with a disability was how would you increase the employment opportunities if people disabilities. By a factor of five or 10. So sort of the typical you know Big Hairy Audacious Goal sort of thing that that that people that slow down. And so. So what we did is is we drove into talking to major employers small or medium enterprises the gig economy. We talked to all ology for the H.R. and sort of human capital fields. And we and we came away after all those conversations. Of course we talked to a ton of people disabilities. We came away from those conversations with about five headlines. Right. The first headline is tech has taken over pretty much all of the H.R. processes of all of you know all these levels of business. So when you're trying to you know do the marketing to get Workology Podcast​ ​| www.workologypodcast.com | @workology
  4. 4. people to apply for a job when people are applying for a job when you're processing you know a candidate that through the interviewing and the selection process when you're onboarding them and when you're trying to maintain them I mean all of that is being powered by technology and the related piece to that is that machine learning is everywhere in that process. Every single distinct task that we could identify in the sort of human capital management tech ecosystem. There is a startup or 10 who are actually promising to use machine learning to do it better to do it cheaper to do it faster and add a side note they are generally these vendors are generally very clueless about diversity and inclusion will come back to that later. Another big issue accessibility is still a big problem while consumer facing companies have pretty much made it part of their standard suite of things they do technically. So they obviously they work in security and privacy so that their customers are users or clients state isn't leaked. But they also work on accessibility so that people disabilities can use their product. Well that happens on the consumer facing side. It doesn't tend to have carried over into the recruitment area and things are far worse on the internal systems side. So you might be a person with disability who could meet and get a job but then when you land inside a company and they say OK here's the toolkit that you're going to use to do this job. And so then you find that you can't. Next piece the data is lousy. We really don't have very good data. And of course it's very difficult to solve a problem. Well when you don't understand it and obviously more data better data helps you understand the problems you can be in to actually make progress against whatever your objective is. The last observation we made is there is a very exciting movement going on throughout certainly almost all large employers which is the diversity inclusion movement. And you know the exciting thing about this is that is that in the main people who've moved beyond compliance EEOC is sort of a a a requirement to do business. People actually see diversity inclusion as a giant business asset that makes their company more powerful. You know if we get more women in our workforce maybe our product won't be terrible for women or this more diverse population that actually represents the markets we're going after. Unfortunately I'd say it seems like at least half the diversity inclusion programs I was talking to didn't include people disabilities in their definition or programming around diversity inclusion. And so people still see the hiring of people disabilities through a compliance lens which is much the way they saw hiring racial minorities 20 or 30 years ago. And I think obviously for for the employment opportunities of people disabilities to really shift in a Workology Podcast​ ​| www.workologypodcast.com | @workology
  5. 5. dramatic way they have to be fully seen as a business asset rather than a compliance issue. Jessica: ​[00:09:59] You said a couple of things. I mean there's a lot of good information here but a couple of things really are top of mind first of all. I love that you're talking about machine learning and as someone who has started and founded seven different companies that are focused on machine learning and I feel like you're uniquely qualified to kind of talk about how it's being applied in the H.R. and recruiting space and not to mention all the work that you guys have done at Bain attack with the research. So I appreciate those insights there. The other thing that I find very interesting is the compliance piece that you're talking about because I do agree with you a lot of people want to say OK. Oh if CCP now required to to look at people with disabilities but not a lot of companies are looking at diversity inclusion like when I look at. Some of the Silicon Valley companies that publish their diversity results people with disabilities aren't included in that reporting. Jim: ​[00:10:58] So you want to dive into those two areas a little bit more. Yeah let's talk a little bit more about that. So as a machine learning experienced person I was kind of surprised both by how extensively machine learning and AI is being applied and how sloppy people were about things that people know about machine learning and and and the the the classic example is sort of like garbage in garbage out. If you're if you train the machine on bad data you get bad results. If you train the machine on heavily biased data the machine faithfully replicates the heavily biased data by making heavily biased predictions. And so everybody who works in machine learning should know these things and be testing for them and controlling for them because they're all the known aspects of what people call artificial intelligence because frankly computers are still dumb as bricks but they're really good and really fast at sort of recreating whatever you feed them. So you know we have some famous examples right. I mean Amazon tried to completely automate their resume a screening process and the tool so extensively discriminated against women that they could not fix it. And Amazon is not known for being a sort of the touchy feely kind of kind of company. And if they felt it was so egregiously bad they couldn't fix it. That says something very serious and I think what what we're seeing though is that the training data that goes into these A.I. systems Workology Podcast​ ​| www.workologypodcast.com | @workology
  6. 6. they're being trained for H.R. processes is almost always dominated by a workforce that is less diverse than the workforce that we want to get to properly less diverse than the workforce that we're sort of legally obligated to have because maybe our workforce reflects discrimination in the past that would be illegal today or was illegal in the day but was never was never you know mediated. So one of the one of the funniest articles that I read was a company came into a they I company came to a an employer and said What's your biggest you know hiring recruiting problem it's like we can't find great salespeople like our current salespeople and they train the data on the top you know 30 salespeople and the machine learning device said well you should hire more guys named Gerard who played lacrosse in high school. Because that's what the patterns were that they found in the data that were most pronounced because I don't know maybe maybe eight. I mean a frat had joined the company at some point and that was the biggest signal they could find in what made it a great salesperson. And that's that's not how we want to actually staff the workforce of the future is is by you know these these very narrow in some ways incredibly stupid patterns that it you know the machine learning can can understand you don't even know how to respond to that. Jessica: ​[00:14:05] That is scary. Jim: ​[00:14:08] Well and it's. And the challenges and is that the vendors. And you know I mean I've been a vendor right. So I I know making claims but I am always cautious about making claims that either I know or false. I think that's a very bad idea. As a business person or should know are false or have been told or false and I don't actually look into it. And so the example of these vendors being told you know your your machine learning tool discriminates against women it discriminates against African-Americans or people color it it discriminates against LGBT people it discriminates against your disabilities. And often the vendors say some variation on. Well we went into this field to make you know a machine learning thing that doesn't see gender doesn't see color doesn't see sexual orientation doesn't doesn't see disability. But what they forget is they choose proxies in their data that are substitutes for gender and ethnicity and sexual orientation and disability. And then the machine discriminates not because it saw these things but because the patterns the data exist. So if you've never ever hired someone who went to let's say some universities that primarily cater to Workology Podcast​ ​| www.workologypodcast.com | @workology
  7. 7. African-Americans and African-American kids it will never be surfaced by the machine if which school you went to is one of the key pieces of data. If you've never ever hired someone from a historically black college or university. So it's some it's very sloppy and I think that I think that many of the machine learning people should be embarrassed. Of course this is not surprising to society at this moment in time when we see you know what Facebook and Google et al have actually done with their terrific machine learning algorithms which are let's say not advancing the objectives of democracy and easy safe living online. Jessica: ​[00:16:16] Agreed. Let's let's talk a little bit about some other findings from the report. The report focused a lot on accommodations specifically when it comes to technology and I love that you guys are focusing on this because in my work with Pete we have started to see so many tech companies that specifically in the age or tech space that aren't providing resources or their tech that that those who have a disability can be able to use. So I wanted to ask you maybe what H.R. and workplace leaders should be doing to help maybe push that along so that companies can think about adding some main features and benefits of their product that people maybe with low vision or sensory or different disabilities can be able to take use and take advantage. Jim: ​[00:17:12] Yeah I think I mean traditionally accessibility pops up after someone is employed and when someone requests an accommodation and usually we think of that as a post you know onboarding or part of the onboarding process as you get to request the accommodation. And I'd say in general that's not going very well. I mean a lot of lot of companies are still maintaining you know paper or spreadsheet kind of records of accommodation requests which is completely different than the modern workforce management sort of system that people are used to today where you know all these capabilities are automated and tracked and measured. And here here's this important essentially you know H.R. kind of capability that is really not done in any systematic ways. I think we saw a giant gap in offering of accommodations during the employment process and when you combine that with machine learning where let's say 80 percent of candidates are screened out before a human ever looks at their resume. The fact that someone this way because they were a bad candidate because they had accessibility problems they didn't get an accommodation. I think that's a that that's a big issue and I Workology Podcast​ ​| www.workologypodcast.com | @workology
  8. 8. think you know and then and then we can go to sort of the acquisitions process. You know when you're buying an HCM system is accessibility for the applicants the employees the people who are using the system the recruiters who are using the system. Is that actually part of the conversation with the vendor. If it's not. Don't be surprised. You end up with a product that doesn't actually meet standards that people would say today are are at least legally required and if not longer required would be actually the reasonable thing to do. So I think this is a this is this is a big issue which is you don't buy an accessible. Product and don't let your internal team build in an accessible product. Knowing what we know now that accessibility is one of those sort of core requirements of any software based system. Well just about any technology system so. Jessica: ​[00:19:21] So Josh and I spoke last year at the SHRM talent conference and we talked about accessibility in the workplace and technology. And we talked we had two different sessions which was fantastic and we have a lot of good discussions. And then as we were kind of heading out of our second session a H.R. tech vendor who will remain nameless came up to us and and in a nice way we had a nice spirited conversation but they basically told us that they're not going to make their technology accessible it's the way it is and it would cost them too much money to be able to do that. How do how do you feel about that. Jim: ​[00:19:58] Well this is essentially the outsourcing of social responsibility and compliance to your customer right. Because it's not hard to imagine especially as this happens more and more often the company that employs that inaccessible product being successfully sued. And and I mean I've talked to some of these you know inaccessible companies and they basically say hey it's not our responsibility it's the responsibility of the employer to offer accommodations so that people can go around our product. And it's like Boy that's a product. But but but very it doesn't come up very often. So the fact that people are starting to ask questions like this you know we we would not dream of buying the H.R. system that leaked employee social security numbers and medical conditions out the side to hackers. Right. That that used to happen. It's now inconceivable. And yet we're doing work we're actually tolerating mainly because of not being aware of people implementing these things that Workology Podcast​ ​| www.workologypodcast.com | @workology
  9. 9. discriminate against minorities and women and people disabilities. But because it's been outsourced to the machine rather than to you know the traditional bigoted you know middle manager making biased decisions. No we just. What we did is we sort of like study you know a whole bunch of big EDS train the machine on it and that's it now. The machines get so it's OK. Right. Jessica: ​[00:21:34] I'm so glad that we're having this conversation really because and it's nice to know that there's people like you in Silicon Valley who are standing up and and trying to to get the word out and I hope that people listen to the podcast here will say Okay I want to push back and ask more questions when I am doing vendor selection or I'm going through my RFP process. Jim: ​[00:21:56] Yeah. And I don't want to make it sound like this is a one sided conversation in Silicon Valley there are terrific companies in in the HCM field that are not doing the minimum going well you know above and beyond those that are aware of these things that care about these issues. And so of course I hope they are more successful with their products out there in the field compared to these people who who kind of ignore these things and take shortcuts because you know that makes them more money. Break: ​[00:22:26] Let's take a reset. This is Jessica Miller Merrill. And you're listening to the work ology podcast. Today we're joined with Jim Jim in a Ben attack about expanding employment opportunities for people with disabilities. This podcast is sponsored by Claire company and is part of our future work podcast series in partnership with the partnership on employment and accessible technology or. Break: ​[00:22:49] This episode has been sponsored by Claire company a complete talent management software provider Claire company software solutions include award winning Applicant Tracking onboarding and performance management solution. Prior retain and engage more top talent with clear company. Workology Podcast​ ​| www.workologypodcast.com | @workology
  10. 10. Jessica: ​[00:23:06] Let's talk about maybe the candidate application process a little bit more. What advice do you have someone maybe who's wanting to make their interview or selection process more accessible. Where should they be starting. Jim: ​[00:23:19] Well I often think in terms of job descriptions at the start often job descriptions are the same one that was there 20 years ago. And so people haven't really visited. What does this job actually require in this day and age. I saw one organization as part of our studies that every single job description had a list of Oh I always take typing your kind of requirements that included the ability to file paper and lift a 20 pound box. I actually do not think that every single information based job involves being able to lift 20 pound boxes. And so you know get rid of those those job requirements that actually aren't real. And of course you know we're also aware of the of the what's it called capacity stacking or qualification stacking you know I need I need to pay more to get the person I need so I'm going to add an extra six qualification and so I can actually justify the price point for this job that actually doesn't require those things so sometimes these issues do run afoul of you know how people actually work the H.R. system but I do think you know seeing what the job description is if if you do use a heavy automated kind of front end like these video interview tools that scared the living daylights out of me think about offering accommodations at the front end so that someone who might run into a tool that they can't use and if it's an app the odds that a lot of people displaced can't use it are pretty high. You know offer an accommodation up front so that you know you're not you basically just aren't throwing people out before they even get a chance to to actually apply. So so those are a couple of the top two points that I would say as part of that process. Jessica: ​[00:25:11] I also will ask you about time assessments because I felt like in my work with Pete that that's also been an area that individuals who are in the hiring process that might have a disability they they don't fare well with timed assessments. Jim: ​[00:25:29] Yeah. No I mean actually a longer time on assessments is one of the top accommodations that people with displays are offered. So so that's just part and parcel of the accommodation. I mean one of the other things that we do is we run the largest library for for people with disabilities online right. So so we're helping high school Workology Podcast​ ​| www.workologypodcast.com | @workology
  11. 11. and college students. And so longer time on tests alternate media. So you know if something is purely visual there should be a textual equivalent if something is purely audio. There should be a a textual column because you know you might have people were blind or deaf but actually the the biggest crowd of people who are likely to be applying for jobs with disabilities that need help or accommodations are people who probably have invisible disabilities that aren't disclosed. And for them let's say someone with a learning disability you know getting 50 percent longer to complete a test could could be you know finding the person who be God's gift to that job. But you know their particular disability gets in the way of you know passing a time test your work and research has touched a great deal on technology and a lot of that stuff's kind of behind the scenes for the H.R. technology founders and leaders and developers who are listening. Do you have any other advice for them on how to maybe think about accessibility for that technology is there building out or that artificial intelligence or machine learning that they're they're putting in to their products. You know 80 percent of the battle's awareness right. There should not be a requirement for H.R. professionals to become super duper machine learning experts. Right. They should they should look to you know whether it's their I.T. teams or their vendors. They should be looking to them to say how do these products help us meet our objective of a more diverse workforce and especially given that we are trying to do extra outreach to these you know more diverse groups. And so. So just by asking those questions I think that's the majority of the battle. There's a huge amount of peer learning surprise. The people who you trust the most to give you you know the news you can use are the people who've been through it before. And so groups like disability M which is the main association of employers who have made you know above and beyond commitments to employing more people disabilities used to be called USPS then they're a great place who've actually said you know here's how I implemented a program to hire more veterans. Which often involves hiring people with disabilities because of the veteran experience over the last 20 years. And so I think a lot of these things are just being on top of it. Asking the question asking the vendor to show how this tool is actually going to be usable by you know not only the people who have the displays that are kind of obvious that you might think of but also might you know how many people with the invisible disabilities but still need you know something extra that allows them to actually demonstrate the value they can bring to the business instead of focusing on the fact that Workology Podcast​ ​| www.workologypodcast.com | @workology
  12. 12. they use a wheelchair or you know or they are they need a work break every you know two hours or whatever it might be. Jessica: ​[00:28:48] Well thank you so much for taking the time to come and talk with us this has been a really great conversation. I wanted to ask you where people can go to learn more about an attack and connect with you and your team. Jim: ​[00:28:59] Well Benetech so Web site is Ben a tech dot org and that's short for beneficial technology. So just be any TCHC talk. And so we do a bunch of technology people displaced we do a lot of other technology for social good and we're always eager to help you know the tech industry do a better job and to help people with disabilities get better opportunity to education employment and full inclusion in society. I love it. Thank you so much Jim. Glad to be here. Closing: ​[00:29:30] The workology podcast Future of Work series is supported by PEAT the partnership on employment and accessible technology PEAT's initiative is to foster collaboration and action around accessible technology in the workplace. Peter's funded by the U.S. Department of Labor's Office of Disability Employment Policy o DEP. Learn more about PEAT and PEAT works dot org. That's PEA t w o R.K. s dot org. Jessica: ​[00:29:58] I've long mentioned that one of the biggest areas of opportunities for employers is expanding into hidden and underserved talent pools one of which is people with disabilities. The challenge for employers isn't just hiring people with disabilities but providing them with the resources tools and technologies to be successful in their jobs. According to a 20 19 disability equality index report only 55 percent of DCI businesses have a company wide external and internal commitment to digital accessibility. This is accessibility throughout the entire employment lifecycle from application to interview to onboard to exit. I love Jim's insights his understanding of machine learning is refreshing not to mention his knowledge of the technology space will include a link to the Benetech report in the transcript. Resources of this podcast a special thank you to our podcast sponsor Claire company and our partnership series partner PEAT thank you for being part of this podcast and we'll see you next time. Workology Podcast​ ​| www.workologypodcast.com | @workology
  13. 13. Closing: ​[00:31:05] Production services for the work ology podcast with Jessica Miller Merrill provided by total picture dot.com.   Workology Podcast​ ​| www.workologypodcast.com | @workology

×