The modern business world is littered with examples where organisations hastily rolled out artificial intelligence (AI) and machine learning (ML)solutions without due consideration of ethical issues, which has led to very costly and painful learning lessons. Internationally, for example, IBM is getting sued afterallegedly misappropriating data from an appwhile Goldman Sachs is under investigation for using anallegedly discriminatory AI algorithm. A closer homegrown example was theRobodebtdebacle, in which the federal governmentdeployed ill-thought-through algorithmic automationtosend out letters torecipientsdemanding repayment ofsocial security payments dating back to 2010. The government settled a class action against it late last year at an eye-watering cost of $1.2 billion after theautomated mailoutssystemtargeted many legitimate social security recipients.
Thattargeting of legitimate recipientswas clearly illegal, says UNSW Business Schools Peter Leonard, a Professor of Practice for the School of Information Systems & Technology Management and the School of Management and Governance at UNSW Business School. Government decision-makersare required by law to take into accountallrelevant considerationsand only relevant considerations, andauthorising automated demands to be made of legitimate recipients was notproper application ofdiscretionsbyan administrative decision-maker.
Prof. Leonard saysRobodebtis an important example of what can go wrong with algorithms in which due care and consideration is not factored in. When automation goeswrong,it usually does soquicklyandat scale. And when things go wrong at scale, you dont need each payout to be much for it to be a very large amount when added together acrossacohort.
Robodebt is an important example of what can go wrong with systems that have both humans and machines in a decision-making chain. Photo: Shutterstock
Technological developments are very often ahead of both government laws and regulations as well as organisational policies around ethics and governance. AI and ML are classic examples ofthisand Prof. Leonard explains there is major translational work to be done in order to bolster companies ethical frameworks.
Theres still a very large gap between government policymakers, regulators, business, and academia. I dont think there are many people today bridging that gap, he observes. It requires translational work, with translation between those different spheres of activities and ways of thinking. Academics, for example, need to think outside their particular discipline,departmentor school. And they have to think about how businesses and other organisations actually make decisions, in order to adapt their view of what needs to be done to suit the dynamic and unpredictable nature of business activity nowadays.Soit isnt easy, but it never was.
Prof. Leonard says organisations are feeling their way to betterbehaviourin this space. Hethinksthat manyorganisationsnow care about adverse societal impacts of their business practices, butdontyet know how to build governance and assurance to mitigate risks associated with data and technology-driven innovation.They dont know how to translate what are often pretty high-level statementsaboutcorporate social responsibility,goodbehaviouror ethics call it what you will into consistently reliable action,to give practical effect to those principles in how they make their business decisions every day. That gap creates real vulnerabilities for many corporations, he says.
Data privacy serves as an example of what should be done in this space. Organisations have become quite good at working out how to evaluate whether a particular form of corporatebehaviouris appropriately protective of the data privacy rights of individuals. This is achieved through privacy impact assessments which are overseen by privacy officers, lawyers and other professionals who are trained to understand whether or not a particular practice in the collection and handling of personal information about individuals may cause harm to those individuals.
Theres an example of how what can be a pretty amorphous concept a breach of privacy is reduced to something concrete and given effect through a process that leads to an outcome with recommendations about what the business should do, Prof. Leonard says.
When things go wrong with data, algorithms and inferences, they usually go wrong at scale. Photo: Shutterstock
Disconnects also exist between key functional stakeholders required to make sound holistic judgements around ethics in AI and ML. There is a gap between the bit that is the data analytics AI, and the bit that is the making of the decision by an organisation. You can have really good technology and AI generating really good outputs that are then used really badly by humans, and as a result, this leads to really poor outcomes, says Prof. Leonard. So, you have to look not only at what the technology in the AI is doing, but how that is integrated into the making of the decision by an organisation.
This problem exists in many fields. Onefieldin which it is particularly prevalent is digital advertising. Chief marketing officers, for example, determine marketing strategies that are dependent upon the use of advertising technology which are in turn managed by a technology team. Separate to this is data privacy which is managed by a different team, and Prof. Leonard says each of these teamsdontspeak the same language as each other in order to arrive at a strategically cohesive decision.
Some organisations are addressing this issue by creating new roles, such as a chief data officer or customer experience officer, who is responsible for bridging functional disconnects in applied ethics. Such individuals will often have a background in or experience with technology, data science and marketing, in addition to a broader understanding of the business than is often the case with the CIO.
Were at a transitional point in time where the traditional view of IT and information systems management doesnt work anymore, because many of the issues arise out of analysis and uses of data, says Prof. Leonard. And those uses involve the making of decisions by people outside the technology team, many of whom dont understand the limitations of the technology in the data.
Why regulatorsneedteeth
Prof. Leonardwas recently appointed to theNSW inaugural AI Government Committee the first of its kind for any federal, state or territory government in Australiatoadvise the NSW Minister for Digital VictorDominelloon how todeliver on key commitments in the states AI strategy.One focusfor the committee ishow to reliably embed ethics in how, when and why NSW government departments and agencies useAIand other automation in their decision-making.
Prof. Leonard said governmentsand other organisationsthat publish aspirational statements and guidance on ethical principles of AIbut fail to go furtherneed to do better.For example, theFederal Governmentsethics principlesforuses ofartificial intelligenceby public and private sector entitieswere publishedover18 months ago, but there is little evidence of adoption across the Australian economy, or that these principles are being embedded into consistently reliable and verifiable business practices, he said.
What good is this? Itis like the 10 commandments.Theyarea great thing. But are people actually going to follow them? And what are we going to do if they dont?Prof. Leonard said it is notworth publishing statements of principles unlessthey are supplemented withprocesses and methodologies for assurance and governance of all automation-assisted decision-making. It is not enough to ensure that the AI component is fair, accountable and transparent: the end-to-end decision-making process must be reviewed.
Technological developments and analytics capabilities usually outpace laws, regulatory policy, audit processes and oversight frameworks. Photo: Shutterstock
While some regulation willalsobe needed to build the right incentives,Prof. Leonard saidorganisations need to first know how to assure good outcomes, before they are legally sanctionedand penalisedfor bad outcomes.The problem for the public sector is more immediate than for the business and not for profit sectors, because poor algorithmic inferences leading to incorrect administrative decisions can directly contravenestate andfederaladministrative law, he said.
In the business and not for profit sectors, thelegalconstraints are more limitedin scope (principally anti-discriminationandscope consumer protection law). Because the legal constraints are limited, Prof. Leonard observed, reporting oftheRobodebtdebacle has not led tosimilarurgency in the business sector asthat inthefederal government sector.
Organisations need to be empowered to thinkmethodically across andthroughpossible harms, whilethere alsoneeds to be adequate transparency in the system and government policy and regulators should not lag too far behind.A combination of these elements will help reduce the reliance on ethics within organisations internally, as they are provided with a strong framework for sound decision-making.And then you come behind with a big stick iftheyrenot using the tools or theyre not using the tools properly. Carrots alone and sticks alone never work; you need the combination of two, said Prof.Leonard.
The Australian Human Rights Commissionsreport on human rights and technologywas recently tabled in Federal Parliament.Human Rights Commissioner EdSantowstatedthat the combination oflearningsfromRobodebtand the Reports findings provide aonce-in-a-generationchallenge and opportunity to develop the proper regulations around emerging technologies tomitigate the risks around them and ensure they benefit all members of the community. Prof Leonard observed that the challenge is as much to how we govern automation aided decision making within organisations the human elementas it is to how we assure that technology and data analytics are fair, accountable and transparent.
Many organisations dont have the capabilities to anticipate when outcomes will be unfair or inappropriate with automation-assisted decision making. Photo: Shutterstock
A good example of the need for this can be seen in the Royal Commission into Misconduct in the Banking, Superannuation and Financial Services Industry. It noted key individuals who assess and make recommendations in relation to prudential risk within banks are relatively powerless compared to those who control profit centres. So, almost by definition, if you regard ethics and policing of economics as a cost within an organisation, and not an integral part of the making of profits by an organisation, you willend up with bad results because you dont value highly enough the management of prudential, ethical or corporate social responsibility risks, says Prof. Leonard. You name me a sector, and Ill give you an example of it.
While he notes that larger organisations will often fumble their way through to a reasonably good decision, another key risk exists among smaller organisations. They dont have processes around checks and balances and havent thought about corporate social responsibility yet becausetheyre not required to, says Prof. Leonard. Small organisations often work on the mantra of moving fast and breaking things and this approach can have a very big impact within a very short period of time,thanks to the potentially rapid growth rate of businesses in a digital economy.
Theyre the really dangerous ones, generally. This means the tools that you have to deliver have to be sufficiently simple and straightforward that they are readily applied, in such a way that an agile move fast and break things' type-business will actually apply them and give effect to thembefore they break things that really can cause harm, he says.
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How to avoid the ethical pitfalls of artificial intelligence and machine learning - UNSW Newsroom