2021/6/19
Japan Association for Philosophy of Science 2021 Annual Meeting
Symposium:
Fairness, Integrity, and Transparency in Formal Systems:
Challenges for a Society Increasingly Dominated by TechnologyTransparency in AI:
Identifying the Real Issue
Takayuki SUZUKI(The University of Tokyo)

This talk is based on the research project“Constructing Philosophy of Artificial Intelligence2.0” funded by JST/RISTEX HITE program.

Recent Progress in AI Research• Games• Image recognition• Machine translation• Autonomous drivingDisputes• Transparency of algorithms• Safety of self-driving cars
• Military use of autonomous agents• Unemployment• Conflict between human beings and AI

Important Distinctions• Kinds of AI:- Artificial super intelligence- Artificial general intelligence- Specialized artificial intelligence

AI

Important Distinctions (contd.)• Kinds of contemporary AI:- AI in general- Machine learning AI- Deep learning AI

MLDL

Limitations of Contemporary AI• Needs large data sets for training(in machine learning).

• Needs labeled data (in supervised learning).
• Has adversarial examples (in deep learning).

Limitations of Contemporary AI (contd.)• Lacks genuine understanding (of words etc.)• Lacks common sense• Lacks the sense of relevanceImplications• The areas where AI, especially deep neural network,performs well are limited.

• Even in the areas where it performs well, it is notclear whether AI and human mind work in the sameway.

- Example: Computer vision and human visionImplications (contd.)• Having body and having experience in the realworld is important (possibly indispensable) togeneral intelligence.

Transparency• Distinctions:- Opacity due to social factors (secret) and opacity due totechnological factors (inscrutability)- Complexity and inscrutability

Transparency (contd.)• Transparency (scrutability) of algorithms:- Some (most) algorithms are transparent.
- Voting algorithms

Transparency (contd.)• Transparency (scrutability) of algorithms:- Even deep neural networks are not completely opaque.
- We know general learning algorithm (back propagationetc.).

- Sometimes we can identify what feature a network
responds to.

Transparency (contd.)• Opacity of algorithms:- Deep neural networks are not always transparent.
- Example: Chess AI.
- What if AI checks the topological relation of 7 pieces
in the last 20 moves?

- Can understand the mechanism in principle. Cannot
predict the response in practice. Cannot give ameaningful interpretation for the feature.

Transparency (contd.)• Transparency in other areas:- Drugs in medicine- Transparency to whom?
- Human decision making- Is the truth of explanation important?
- Should we care more about transparency here?

Transparency (contd.)• A trade-off between transparency and performance.

Transparency (contd.)• A trade-off between transparency and performance.
• When, why, and to what extent should AI betransparent?

Biases• Whose biases?
- In most cases, biases come from the data.
- We might be able to use AI to find biases in our society.
- Example: Word2Vec• What is the problem of biases that is unique to AI?

Biases (contd.)• The problem of value-ladenness:- ‘Fairness’ in voting algorithms- A more subtle case: Bias in COMPAS(Sumpter 2018, Ch.6)

Biases (contd.)• The problem of emergent biases:- The Google Photo case (Mitchell 2019, Ch.6) :- No biases in the data.
- No biases in the algorithm.
- Cases like this, as well as adversarial examples,suggest that AI and human mind might notwork in the same way.

A Positive Perspective• Two conceptions of AI:- AI as a substitute for human beings- AI as a complement for human beings

A Positive Perspective (contd.)• Overcoming human weaknesses with AI:- Human weaknesses: Poor logical reasoning,slow processing speed, poor memory,
fatigue, cognitive biases…

- Where we perform poorly is where AI
performs well.

- We may be able to overcome ourweaknesses with the help of AI.

Conclusion• Morals:- We have to distinguish the problems originatedin AI itself and the problems originated in humansociety.

- We have to look at the cases outside of AI to beconsistent about the problem of transparency.

- We may be able to identify our own biases andother problems through AI.

- We should use AI so that we can overcome ourweaknesses.