AIES : how we talk about AI, algorithmic fairness, norms and explanations

My brief notes from today’s talks: for more details, check the program.

Ryan Calo: How we talk about AI (and why it matters)

There are several studies which demonstrate the ways in which language might shape approaches to policy. For example, one showed that people were more likely to recommend punitive measures when a threat was described as “a predator stalking the city”, rather than “an illness plaguing the city”.  There are legal precedents in the US of language about “robots” being a way to talk about people who have no choice, (and therefore liability).

A whole lot of drones in the sky above treesCalo notes that there are some trends in AI that he’s “upset about but not going to discuss at length, particularly the tendency for both supporters and critics of AI talk about it as if it’s magic. For example, Calo mentioned a billboard displaying a line of identical people with backpacks claiming that, “AI has already found the terrorist.” On the other hand, we should consider language about “killer robots coming door to door to kill us” with caution.

Rhetorical choices about AI policy influence policy, often in very subtle ways. For example, do we talk about AI research as a “race” or do we talk about it as a global collaborative effort that works towards human flourishing? And how do these different frames shape different concrete policies? Current US policy (including restrictions on sharing particular technologies) only make sense if we understand AI research as a high-stakes competition.

Language around “ethics” and “governance” also plays a role here. This rhetoric is familiar, and therefore palatable. Efforts to bring in ethical governance of AI research is laudable. Ethics has a critical role in shaping technology. However, we should also pay attention to the power of these words. Before we start imposing requiremlaents and limits, we need to be sure that we actually understand the ethical frameworks we’re working with.

Both proponents and critics of AI think that it will change everything. We should be thinking about a hypothetical future existential threat posed by AI, but we should also be thinking about more immediate concerns (and possibilities?). If it’s true that AI is the next world-shaping technology, like the steam engine, then policy needs to shift radically to meet this. And we need to start changing the way we talk. That project begins with conferences like this one.

We should also be looking at specific measures, like impact assessments and advisory bodies, for implementing AI tools. Unfortunately, the US government will probably not refrain from the use of any AI weapons that are seen to be effective.

We absolutely should be talking about ethics, guided by the folks who are deeply trained in ethics. Lawyers are contractors building the policies, but ethicists should be the architects.

Note: One of the main questions that I have regarding Calo’s talk, and that Peter and I partially – albeit implicitly – address in our own talk, is how we decide who counts as ‘deeply trained in ethics’ and how the AI community should reach out to ethicists. There is an ongoing under-representation of women and minorities in most university philosophy departments. Mothers (and not fathers) are also less likely to be hired and less likely to progress within academia. This is partially shaped by, and shapes, dominant framings of what is valued and promoted as expertise in ethics. This is fairly obvious when we look at the ethical frameworks cited in AI research ethics: most philosophers cited are white, male, and Western.

The spotlight session giving brief overviews of some of the posters presented included a few that particularly stood out (for various reasons) to me:

  • In ‘The Heart of the Matter: Patient Autonomy as a Model for the Wellbeing of Technology Users‘, Emanuelle Burton, Kristel Clayville, Judy Goldsmith and Nicholas Mattei argue that medical ethics have useful parallels with AI research. For example, when might inefficiency enable users to have an experience that better matches their goals and wishes?
  • In ‘Toward the Engineering of Virtuous Machines‘, Naveen Sundar Govindarajulu, Selmer Bringsjord and Rikhiya Ghosh (or maybe Hassan?) talk about ‘virtue ethics’: focus on virtuous people, rather than on actions. Eg. Zagzebski’s Theory: we admire exemplar humans, study their traits, and attempt to emulate them. (I’m curious what it would look like to see a machine that we admire and hope to emulate.)
  • Perhaps the most interesting and troubling paper was ‘Ethically Aligned Opportunistic Scheduling for Productive Laziness‘, by Han Yu, Chunyan Miao, Yongqing Zheng, Lizhen Cui, Simon Fauvel and Cyril Leung. They discussed developing an ‘efficient ethically aligned personalized scheduler agent’ will can workers (including those in the ‘sharing’ economy) work when they are highly efficient and rest when they’re not, for better overall efficiency. Neither workers nor the company testing the system were that keen on it: it was a lot of extra labour for workers, and company managers seemed to have been horrified by the amount of ‘rest’ time that workers were taking.
  • In ‘Epistemic Therapy for Bias in Automated Decision-Making’, Thomas Gilbert and Yonatan Mintz draw on distinctions between ‘aliefs‘ and ‘beliefs’ to suggest ways of identifying and exploring moments when these come into tension around AI.
The second session, on Algorithmic Fairness, was largely too technical for me to follow easily (apart from the final paper, below), but there were some interesting references to algorithms currently in use which are demonstrably and unfairly biased (like COMPAS, which is meant to predict recidivism, and which recommends harsher sentences for minorities). Presenters in this panel are working an attempts to build fairer algorithms.
In ‘How Do Fairness Definitions Fare? Examining Public Attitudes Towards Algorithmic Definitions of Fairness‘, Nripsuta Saxena, Karen Huang, Evan DeFilippis, Goran Radanovic, David Parkes and Yang Liu discuss different understandings of ‘fairness’. This research looks at loan scenarios, drawing on research on Moral Machines. It used crowdsourcing methods via Amazon Turk. Participants were asked to choose whether to allocate the entire $50,000 amount to a candidate with a greater loan repayment rate; divide it equally between candidates; or divide the money between candidates in proportion to their loan repayment rates.
There are three different ways of understanding fairness examined in this paper:
  • meritocratic fairness,
  • treat similar people similarly,
  • calibrated fairness.
This research found that race affected participants’ perceptions of fair allocations of money, but people broadly perceive decisions aligned with ratio to be fairest, regardless of race.
The presenters hope that this research might spark a greater dialogue between computer scientists, ethicists, and the general public in designing algorithms that affect society.
Session 2: Norms and Explanations
Learning Existing Social Conventions via Observationally Augmented Self-Play, Alexander Peysakhovich and Adam Lerer
This looks at social AI. At the moment, social AI is mainly trained through reinforcement learning, which is highly sample inefficient. Instead, the authors suggest ‘self play’. During training time, AI might draw on a model of the world to learn before test time. If self-play converges, it converges at a Nash equilibrium. In two-play zero sum games, every equilibrium strategy is a minimax strategy. However, many interesting situations are not two-player zero-sum games, for example traffic navigation. The solution to this is: quite technical!
Legible Normativity for AI Alignment: The Value of Silly Rules, Dylan Hadfield-Menell, Mckane Andrus and Gillian Hadfield
A lot of conversations right now focus on how we should regulate AI: but we should also ask how we can regulate AI. AIs can’t (just) be give the rules, they will need to learn to interpret them. For example, there’s often a gap between formal rules, and rules that are actually enforced. Silly rules are (sometimes) good for societies, and AIs might need to learn them. Hadfield discusses the Awa society in Brazil, and what it might look like to drop a robot into the society that would make arrows (drawing on anthropological research). Rules include: use hard wood for the shaft, use a bamboo arrowhead, put feathers on the end, use only dark feathers, make and use only personalised arrows, etc. Some of these rules seem ‘silly’, in that more arrows are produced than are needed and much of hunting actually relies on shotguns. However, these rules are all important – there are significant social consequences to breaking them.
A 1960s advertisement for "the Scaredy Kit", encouraging women to start shaving by buying a soothing shaving kit.This paper looked at the role of ‘silly rules’. To understand this, it’s useful to look at how such rules affect group success, the chance of enforcement, and the consequences for breaking rules. The paper measured the value of group membership, the size of the community over time, the sensitivity to cost and density of silly rules. As long as silly rules are cheap enough, the community can maintain its size. It’s useful to live in a society with a bunch of rules around stuff you don’t care about because it allows a lot of observations of whether rule infraction is punished. AIs may need to read, follow, and help enforce silly as well as functional rules.
Note: Listening to this talk I was struck by two things. Firstly, how much easier it seems to be to identify ‘silly’ rules when we look at societies that seem very different from our own. (I think, for example, of wondering this morning whether I was wearing ‘suitable’ conference attire, whether I was showing an inappropriate amount of shoulder, and so on.) Secondly, I wondered what this research might mean for people trying to change the rules that define and constrain our society, possibly in collaboration with AI agents?
TED: Teaching AI to Explain its Decisions, Noel Codella, Michael Hind, Karthikeyan Natesan Ramamurthy, Murray Campbell, Amit Dhurandhar, Kush Varshney, Dennis Wei and Aleksandra Mojsilovic
Understanding the basis for AI decisions is likely to be important, both ethically and possibly legally (for example, as an interpretation of the GPDR’s requirements for providing meaningful information about data use). How can we get AI to meaningfully explain its decisions? One way is to get users (‘consumers’) to train AI about what constitutes a meaningful explanation. The solution to this is: quite technical!
Understanding Black Box Model Behavior through Subspace Explanations, Himabindu Lakkaraju, Ece Kamar, Rich Caruana and Jure Leskovec
Discussing a model for decisions on bail. Important reasons to understand the model’s behaviour:
  • decisions-makers readily trust models they can understand,
  • it will allow decision-makers to override the machine when it’s wrong,
  • it will be easier to debug and detect biases.

How to facilitate interpretability? The solution to this is: quite technical!

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