June 19th JAPS-DLMPST Symposium ５人の講演アブストラクト"Transparency in AI: Identifying the Real Issue"
Takayuki Suzuki (The University of Tokyo)
アブストラクト 日本語版
人工知能は不透明である、そしてそのことが社会にさまざまなリスクをもたらすとしばしば言われる。しかし、人工知能がどのような意味で不透明であるかは、かならずしも明らかではない。多くの古典的なアルゴリズムは透明である。また、不透明な人工知能の典型と考えられている深層ニューラルネットワークの働きについても、さまざまな説明を与えることが可能である。さらに、人工知能以外の領域においては、われわれは透明性をそれほど重視しないこともある。とはいえ、人工知能の働きが理解できない事例が存在することは間違いない。このことは、深層ニューラルネットワークにおいて、しばしば予期せぬ反応やバイアスを見出す理由ともなっている。それゆえ、人工知能に固有の透明性の問題が存在することは間違いないように思われる。それがどのような問題であるかを明確化するには、さらなる検討が必要である。
人工知能と同様、人間の心の働きも、しばしば不透明であり、さまざまなバイアスや欠点をもっているが、人間の弱みや強みと人工知能の弱みや強みは相補的な関係にある。それゆえ、われわれは、人間の知能をコンピュータによって再現することよりもむしろ、人工知能を活用して人間のもつバイアスや欠点を克服することを目指すのが生産的であるように思われる。
Abstracts of the (first-round) talks…
Invited Talk 1“Legitimacy, Authority, and the Political Value of Explanations”
Seth Lazar (Australian National University)
As rapid advances in Artificial Intelligence and the rise of some of history's most potent
corporations meet the diminished neoliberal state, we have become increasingly subject topower exercised by means of automated systems. Machine learning, big data, and related
computational technologies now underpin vital government services from criminal justice totax auditing, from public health to social services, from immigration to defence. Two-sided
markets connecting consumers and producers are shaped by algorithms proprietary tocompanies such as Google and Amazon. Google's search algorithm determines, for many ofus, how we find out about everything from how to vote to where to get vaccinated; Facebook,Twitter and Google decide which of our fellow citizens' speech we get to see—both what getstaken down, and (more importantly) what gets promoted. We sometimes imagine AI as a far

off goal—either the handmaiden to a new post-scarcity world, or else humanity's apocalyptic'final invention'. But we are already using AI to shape an increasing proportion of our online
and offline lives. As the pandemic economic shock ramifies, and the role of technology in ourlives grows exponentially, this will only intensify.
We are increasingly subject to Automatic Authorities—automated computational systems thatare used to exercise power over us, by substantially determining what we may know, what we
may have, and what our options will be. These computational systems promise radicalefficiencies and new abilities. But, as is now widely recognised, they also pose new risks. In
this paper I focus on one in particular: that the adoption of Automatic Authorities leads us tobase increasingly important decisions on systems whose operations cannot be adequatelyexplained to democratic citizens.
Philosophers have long debated the importance of justifications in morality and politics, butthey have not done the same for explanations. What's more, the most prominent AutomaticAuthorities in our lives today are deployed by non-state actors like Google and Facebook, andanalytical political philosophy has focused much more on state than non-state power. To makeprogress on one of the most pressing questions of the age of Automatic Authorities, therefore,
we must make substantial first-order progress, on two fronts, in moral and political philosophy.
That is my goal in this paper. My central claim: only if the powerful can adequately explain
their decisions to those on whose behalf or by whose licence they act can they exercise power
legitimately and with proper authority, and so overcome presumptive objections to their
exercise of power grounded in individual freedom, social equality, and collective selfdetermination. This applies to all authorities, not only automatic ones. But I will demonstrate
its application to Automatic Authorities, including those sustained by non-state actors. I will
use this account of why explanations matter to address the urgent regulatory questions of towhom explanations are owed, and what kinds of explanations are owed to them.
Invited Talk 2“Explanation: from ethics to logic”
Gilles Dowek (INRIA and ENS Paris-Saclay)
Abstract: Explaining decisions is an ethical necessity. For example, neither a person nor apiece of software ought to reject a bank loan application, without providing an explanation forthis rejection. But, defining the notion of explanation is a challenge. Starting from concrete
examples, we attempt to understand what such a definition could look like.

A usual definition assumes that what is explained is a statement and what explains it is a logical
proof of this statement. For example, a proof in elementary geometry both shows that thestatement is true and explains why it is true. But this definition is not sufficient, as somelogical proofs are seen as more explanatory than others.
In this talk, we give two successive definitions of the notion of explanation. The first keeps
the idea that an explanation is a logical proof, but the explanatory character of the a proof
relies on the fact that it contains a cut: the proof of general statements followed by aspecialization to a particular one. So, the fact that a proof is explanatory is measured by thedegree of generalization it allows. The second definition uses the algorithmic interpretationof proofs to generalize this definition. An explanation is then a pair formed with a short, fast,
and wide algorithm and an input value for this algorithm.
Invited Talk 3“Justified Representation in Approval-Based Committee Voting”
Edith Elkind (University of Oxford)
Computational social choice is a rapidly growing research field that studies algorithmic
aspects of collective decision-making and preference aggregation. It studies questions suchas: can we quickly compute the outcome of a given voting rule? For a given voting rule, can astrategic voter efficiently compute her optimal strategy? Is there a voting rule that satisfies aparticular set of desirable properties (axioms) and admits an efficient winner determination
algorithm?
In this talk, we consider these questions in the context of multiwinner voting rules withapproval ballots. We formulate a fairness axiom, which we call Justified Representation, as
well as a strengthening of this axiom. We identify voting rules that satisfy the JR axiom andinvestigate their algorithmic complexity. Finally, we propose a tractable rule that satisfies thestrong version of the axiom.

Two Invited Discussant’s Talks
Invited Discussant’s Talk 1“What are explanations worth in science?”
Minao Kukita (Nagoya University)
Steven Weinberg argues that the exemplar of physics is "a simple set of mathematicalprinciples that govern a wide range of phenomena with precision. As can be seen here,

"generality," "precision," and "simplicity" are highly thought of in science. Then, why are theyimportant in science? In this talk, I will discuss the value of generality and simplicity inparticular from the perspective of the importance of information communication in science.
Thus, we will focus on the value of explanations in science in reducing the cognitive cost ofsharing and applying information.
I will argue that the increasing use of artificial intelligence based on big data in the practiceof science means that information will no longer be shared and applied in the traditional way.
Scientific explanations will then become irrelevant in terms of saving costs in information
communication. However, scientific explanations also have aesthetic value beyond mere
means of saving cognitive costs.How the perception of such aesthetic value will change in thefuture is an important topic, and I would like to call on people working in the fields ofphilosophy of science, sociology of science, and STS to explore this topic.
Invited Discussant’s Talk 2"Transparency in AI: Identifying the Real Issue"
Takayuki Suzuki (The University of Tokyo)
It is often argued that AI lacks transparency and that this might be a potential risk to oursociety. It is not clear, however, in what sense AI lacks transparency. Many classical algorithms
are transparent. Even in deep neural networks, which are typically seen as opaque, we canhave some explanation of how they work. Moreover, in areas other than AI, we often don't
care much about transparency.
It is true, however, in some cases, how AI works is inscrutable. This is why we sometimes findunexpected responses and biases in AI, especially in deep neural networks. So, it seems that
there really is a problem of transparency that is unique to AI. We need more work to identify
what it is.
It also should be noted that human mind, as well as AI, is often opaque and biased. It will be
more productive if we try to overcome our weaknesses with AI, rather than try to replicate ourintelligence with AI, because the weaknesses and the strengths of human mind and those ofAI complement each other.