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  • hi and welcome to the Machine ethics podcast this month I'm talking with rod

  • McCargow director of AI and PWC I met up with rod at PWC office in London and we

  • chatted about modeling unintended consequences, AI ethics audits, working

  • with dubious companies and intentions, what we should be teaching our children

  • and future careers a recipe for AI future mitigating job displacement and

  • other AI for good topics. If you like this podcast then check out the other

  • episodes at machine-ethics.net or you can contact us at hello@machine-ethics.net

  • you'll find us at Twitter, Instagram and YouTube, to support the

  • podcast go to patreon.com/machineethics extended interviews reviews and

  • more my thoughts on the episodes and AI topics and news of the month, thanks

  • again to Rob and hope you enjoy

  • Hi Rob thanks for joining me on the podcast

  • thank you having week could you introduce yourself and what you do

  • absolutely so I'm the director of artificial intelligence at PwC in the UK

  • so our team is basically and tasked with applying the technology across the

  • breadth of our organization on both internal projects but also working with

  • that clients across all industry sectors on solving some of their hardest

  • business problems as well using different forms of AI and a lot of the

  • other things I'm involved with also involve working with governments around

  • the world on the impacts on national strategy and policy and as part of our

  • assets on the advisory board of the All Party Parliamentary Group on on AI

  • amongst other appointments yes so it's been said that you're the nicest man in

  • AI how'd you feel about that depends who said it well it's just first I've heard

  • so on that point Robin to you kind of what is AI when you're talking about AI

  • what are you talking about more specifically well I think it depends on

  • the the audience that we're dealing with at the time and if we're working with

  • clients across different corporate functions across HR for example and

  • compliance maybe we don't necessarily get into a deep deepest of technological

  • descriptions but for me I think it's a high level to differentiate from

  • technology of old technology that falls into the AI domain of technologies that

  • can sense think act and through an iterative feedback loop learn and they

  • clearly sit as interesting bedfellows along more mature technologies such as

  • robotic process automation for example but we tried to focus across the breadth

  • of the main AI technologies but if I'm being candid the very first thing I do

  • in any of these things is state that I keep the job title to get me into the

  • room but the first thing I do is say they are doesn't really exist we have

  • this assembly of really interesting technologies on the pinnate from machine

  • learning and deep learning to natural language processing and generation and

  • other techniques that make up this AI family yeah so so the AI of

  • kind of science fiction doesn't exist necessarily but you've got this kind of

  • suite of things which go under that banner at the moment indeed yeah

  • great and we were talking briefly before but kind of how does PwC fit in with

  • this how they're talking about a I and and what they're doing in anyway I guess

  • as well well I think where right now is we've seen amazing breakthroughs of the

  • technology in in consumer use cases in use cases of fascinating utility but not

  • necessarily a huge amount of consequence on people's lives

  • so there's fantastic things being served up through iron maps or movie

  • recommendation engines and all sorts of ecommerce types of applications I think

  • where we're starting to see businesses in for example heavily regulated

  • industries healthcare financial services banking insurance et cetera criminal

  • justice starting to wrestle with this technology realizing that this has a

  • profound impact on their business they have to get moving on starting to

  • embrace and adopt but by doing so this opens up this whole cupboard of new

  • risks which I'm sure we'll get into over the course of the conversation today so

  • for us I think because we're already working with just about every

  • organization across the across the land it's some capacity are the auditing or

  • advising them in some capacity we're often on-site there is the trusted

  • advisor to help debunk some of the myths ology provide the right level of comfort

  • and confidence around the tech and allow them to get started and start moving a

  • pace with the innovation offered by AI so I see you a lot at

  • these sorts of conversations that you mentioned the kind of what happens when

  • you have these sort of technologies in those places in healthcare in the

  • justice system oh there's something like top-level I mean obviously this machine

  • at these forecasts and we took up a lot about this sort of things is there some

  • of the things which you're keen on like things that you are interested in

  • talking about in terms of those sorts of ethical issues yeah I mean I think be

  • more led by the you know the explosion in these events that I get the privilege

  • to go and speak at and and I think judging by the Q&A after them the two

  • areas that seem to elicit by some comms severable distance the most interest and

  • the most inquiry first of all I think is around the impact on the workforce

  • through automation through human machine interaction and through education skills

  • and future proofing of careers yeah I think that's one big category that

  • always creates huge amount of interest and then anything else that falls into

  • that AI ethics bucket is again of significant interest and and that's for

  • me is is a fascinating area and as we start seeing this started to scale in in

  • these use cases of significant consequence this brings a whole level of

  • interest across the breadth of different corporate functions to make sure that

  • people are fully conversant with the implications on their business yes II

  • think it's really important that those business leaders are appreciate the

  • technology and they might not have a low level understanding but if they're going

  • to apply it then they better well know what they're applying yeah this this is

  • now an absolute necessity rather than a nice-to-have this is a fundamental

  • prerequisite for a four up for a executive C suite member of a board for

  • example because this these specific use cases will more often not rear their

  • head in their departments and I think the one that I found very interesting to

  • look at and make sure that we're clear focused on are some of the the HR

  • applications you and I monitor this the press and the media quite a bit around

  • keeping up to date and what's happening and the ones that seem to constantly

  • rear their heads like social media where we first met I think are are those sort

  • of ones around recruitment for human performance type of monitoring systems

  • and as a consequence you know people like HR directors absolutely have to get

  • to grips with this technology and quickly yeah

  • or and there's some stories there of how that's been negative or like done not

  • necessarily really badly but like in a you know kind of good and evil sort of

  • way but like dubiously something that maybe we don't want to promote

  • thing and there's the Amazon example comes to mind about having you know

  • promoting men in their CV machine learning tactics and things of that and

  • because of past bias data than this little thing so it's really about

  • getting around that sort of Missy misunderstanding

  • maybe not the malicious use but like a stupidity in these high stakes arenas

  • but yeah I'm a big believer that the substantial majority of people were

  • configuring and deploying these systems are coming in with the best of

  • intentions yes with with good values more often not but and we we I think we

  • see where they don't always work out well they don't always leads to the best

  • outcomes they often can lead to amplification of bias and discrimination

  • for example and typically affecting vulnerable groups of they called us or

  • all customers it is often because that there's not been the right mixture of

  • people in the room to provide the right level of challenge and I think on the

  • one hand we very well aware that there's a substantial issue around the

  • homogeneity of the workforce you know it is very well noted that it's extremely

  • white a male which there's a lot of great corporate ishutin cluding some of

  • ours to try to address that imbalance but I think I'm also looking at the

  • Disciplinary homogeneity as well and it's absolutely critical to out there

  • are people in the room to give that level of challenge and and that go/no-go

  • power of veto and for example when wherever configuring a specific use case

  • in our team will always make sure we have the right subject matter experts in

  • the room and and even beyond that in fact I've been talking about ethics and

  • AI in business for quite a number of years now and I thought about it

  • actually take some action on this and that we've just hired our first AI

  • ethicist to the team months ago cool who we we know with the level of rigor we

  • face as a organization and scrutiny we know that we're confident that we meet

  • the high standards around data security and privacy around regulatory compliance

  • around around you know the whole issues of risk and quality

  • but specifically with regards to ethics that need to now think through not just

  • secondary but tertiary unintended consequences it's now critical that

  • giving people that power and freedom to explore investigates and model and

  • challenge I think it is something really valuable now and and that's you know

  • giving us that interesting new type of job they'll be talking about the jobs of

  • the future what we've just created you know anyone for our organization so

  • prove it's gonna happen yeah that's great they what's that kind

  • of remit is it kind of like future rising or is it more like philosophy or

  • people yeah this twenty word Lander in in the real life world of what's

  • expected yeah which is actually that's what I mean yeah London in the kind of

  • reality of what's happening on the ground I mean really we have the

  • opportunity to meet some of these for a meet the world-class academics and

  • philosophers and ethicists working on this and you know it's fascinating I've

  • learned a lot in recent years but if you sitting there in business making random

  • decisions to drive profit or to reduce cost or future prove the organization

  • there's maybe not the same man you ship of deliberation that happens and if you

  • think about ethics specifically with of course in this explosion of publication

  • of new ethical principles in the last two years in particular I think at the

  • last count we'd we'd come across the in excess of 70 if you add together the big

  • tech companies the World Economic Forum the I Triple E the baking principles it

  • having all these together yeah you've got a lot of material out there and we

  • have reality of businesses going to be able to read all of those and discern

  • which one is most appropriate for their particular geography and setting yeah

  • and and acts accordingly and so what we what we have is we actually have read

  • all these whole team on it and went in with a fine-tooth comb and built

  • effectively a traceability matrix so what we can now be able to say to

  • clients through what we call our responsible AI approved

  • is okay we feel that with for the right governance in place around the project

  • its had the right approach in terms of identification and the biasing of

  • datacenters prior to training we're confident that it's appropriately

  • scrutinized from a security and privacy perspective and for this particular use

  • case it's got the appropriate level of interpret ability and explain ability

  • now moving beyond that we can say it's got this relatively clean bill of health

  • with the caveat to give you the confidence to move forward now there's a

  • conscious decision to make as a leadership team running these projects

  • to say what do we optimize this solution for is it to maximize profit performance

  • is there a trade-off to be made that allows you to drive even

  • further transparency into the system and you want to then optimize for fairness

  • and the fairness today is fascinating I think even more fascinating than ethics

  • there was a piece of work enough maybe you still could share the the link so

  • you can share with you regionally we did a piece on this and we found that

  • fairness is something that's constantly raised in all of these ethical principle

  • documents yeah and the very high level ones are all very laudable and they stay

  • on ethics so AI should be benevolent it should be good for Humanity it should be

  • transparent it should be fair and inequitable eccentrics yeah but are you

  • get to fairness there's in excess of 20 mathematical definitions of fairness so

  • if you then just take that at all who's it fair to yeah you can't be fair

  • universally to every single person in society

  • yeah therefore do you have to define who it's fair to yes so when you get into

  • those sort of conversations you can really make sure that the projects are

  • proceeding with that level of rigor of conversation certainty buy-in and a

  • conscious choice around what the project is is optimized to do yeah and there are

  • sort of things that the our own ethicists will

  • the ability to shape and starless conversations for our own projects and

  • clients that we work with the fairness thing is really interesting I think that

  • comes that's part of the ethical conversation because what you know like

  • the morality of an individual like what is fairness to you does that actually

  • warrior she told me about when we say fairness is a really interesting point

  • if if a a company came to you and went oh this is so great but we actually

  • trying to optimize for you know the the outcome the the monetary return and

  • actually maybe the fairness is not on a high or an agenda

  • well the sorts of conversations do you have there yes quite difficult I mean I

  • think I don't think I don't think we've had them like that yet but I think it's

  • an interesting question to raise alphabetically as as the market becomes

  • more sophisticated I think there's two things there I think that there there

  • may well get to the point where there's certain use cases and applications that

  • it's simply not appropriate to go near the certain industries that might be

  • more difficult than others to work with but I think we also have to respect the

  • fact that to attract the very best talent the best talent want to be

  • applying their skills to the you know the the appropriate use cases and with

  • that in mind giving people the the right to to not have to time partake in

  • certain projects is something I think that's getting slightly comfortable with

  • yes oh and we've seen this haven't we in that last a year with how breaks of

  • employee activism which i think is something which your organization's need

  • to take into account around so what we want to be aligned with doing yeah and

  • it's I mean for me it's look it's laudable that people who are taking

  • these sorts of actions in the face of things which go against their principles

  • they're you know internal I spoke to one of those people not so long ago actually

  • who Jack Paulson I don't know yeah of young he quit Google because of the the

  • arm stuff and one was the one of the first people to do so and then there was

  • all this action afterwards so it's an interesting thing that's happening where

  • people starting to take notice of how these technology

  • applied and when it's appropriate to do so do you have any kind of like hardline

  • ideas of what maybe isn't appropriate or having a specific yeah they think we've

  • got for where it's kind of like you know a list of no go no go I mean personally

  • this things that I you know don't feel comfortable with morally or that's or

  • Thomas weapons for example those conversation coming back to that's a

  • good point no coming back to your conversations

  • that you might have in government with the AP PGA I'm do you have these little

  • conversations about I mean obviously the general AI conversation has been hand

  • there but do you have the kind of robotics conversation about where when

  • isn't appropriate to use these sorts of technologies at the moment with the

  • government you know what I think the thing that actually gives me a lot of

  • optimism and and professional pride working in this part of the world around

  • this topic is that we've got a really quite sophisticated community that's

  • been active now for the best part of say two-and