Ep 184 - Employment Success for People with Disabilities

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

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.

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

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

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

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

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

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

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.

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

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

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.

Closing: [00:31:05] Production services for the work ology podcast with Jessica Miller
Merrill provided by total picture dot.com.


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