Section outline
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Course overview
This course will give students the tools to understand the main ethical and legal concerns surrounding AI, empowering students to incorporate ethical and regulatory constraints in their design of AI solutions, an approach called "human-centric by design", or "ethics by design".
The course schedule is as follows:
Date (Tuesdays 13:30 - 16:45)
Teacher
Key points covered
23/11/2021
AI ethics, fundamental rights and law
Winston Maxwell
· Difference between ethical principles and law
· Fundamental rights texts affecting AI
· Managing ethical/fundamental rights tradeoffs in AI
· Introduction to the proposed EU AI Regulation
30/11/2021
Privacy and data security Fabian Suchanek
· ensuring security, challenges of anonymization
07/12/2021
Privacy and data security -2
Fabian Suchanek
Fun hackathon
14/12/2021
Bias and Fairness
Sophie Chabridon
-Definitions
-Individual and group fairness
-Sources of discrimination: bias in data, bias in algorithms
-State of the art solutions:data diversity, algorithm transparency
-Discussion of research papers
11/01/2022
Bias and Fairness -2
Sophie Chabridon
18/01/2022
Social impacts
Ada Diaconescu
social side effects: relations, society, economy, cognitive, employment. Class discussions.
25/01/2022
AI ethics use cases
Winston Maxwell
Autonomous lethal weapon systems
Facial recognition
Social media
Autonomous vehicles
AI and health
01/02/2022
Final exam
Over-arching AI Ethics Themes
Below are some over-arching themes that I’ll ask you to keep in mind throughout the course. I'll ask each student to prepare an individual poster to present a use case or anecdote highlighting one of the themes below.
The overarching themes are the following.
I. AI and the effect on work
a. AI replacing workforce
b. What is the role of work in human existence?
c. AI for recruiting
d. Amazon Mechanical Turk
II. AI and the surveillance state
a. “Surveillance capitalism” (Shoshana Zuboff) – transforming ubiquitous surveillance and data gathering into business opportunities (Facebook and Google)
b. Surveillance by government: predictive policing, facial recognition, algorithms to detect terrorist threats: how to draw the right balance between privacy and public security
III. AI and health
a. Individualized, predictive medicine
b. Epidemic (COVID) management
c. Neuralink
d. Augmented humans, transhumanism
e. Robot doctors
IV. AI and democratic institutions
a. AI and manipulation of populations
b. Fake news, polarization, and the “post-truth” era
c. Election manipulation, social (cyber) warfare
d. Freedom of expression vs censorship
V. AI and human dignity
a. Autonomous lethal weapons: the respective role of humans and machines in warfare
b. Robot judges – can humans be judged by a machine? (cf. Estonia robot judges experiment)
VI. AI and discrimination
a. Racism, gender inequality, social inequalities. Does AI make societal discriminations worse? Can AI help offset human discriminations?
VII. AI and the end of serendipity
a. What is the role of chance in our lives, careers, scientific discoveries? By reducing the role of chance, does AI harm innovation and personal development?
b. Can chance be a justifiable solution for ethical dilemmas such as the trolley problem? (Alexei Grinbaum)
c. The role of outliers (“black swans”) in human development.
VIII. AI and human psychology
a. Human machine interactions - how can AI make humans smarter (and not dumber)
b. Robot companions, robot ‘emotions’
c. Social engineering - nudges to help affect human behavior: eg « you haven’t been walking enough today… »
IX. AI and safety certification
How does machine learning change our approach to certifying safety-critical systems?
What AI-related safety lessons can we learn from the Boeing 737 Max failures?
X. Can AI save humanity from itself?
a. AI and climate change
b. AI “taking control”: Isaac Asimov laws of robotics, 2001 Space Odyssey, etc.
The 23 Asilomar Principles
Developed in 2017, the Asilomar principles remain today one of the best texts on AI ethics
Research Issues
1) Research Goal: The goal of AI research should be to create not undirectedintelligence, but beneficial intelligence.
2) Research Funding: Investments in AI should be accompanied by funding for research on ensuring its beneficial use, including thorny questions in computer science, economics, law, ethics, and social studies, such as:
How can we make future AI systems highly robust, so that they do what we wantwithout malfunctioning or getting hacked?
How can we grow our prosperity through automation while maintaining people’sresources and purpose?
How can we update our legal systems to be more fair and efficient, to keep pace with AI, and to manage the risks associated with AI?
What set of values should AI be aligned with, and what legal and ethical statusshould it have?
3) Science-Policy Link: There should be constructive and healthy exchange between AI researchers and policy-makers.
4) Research Culture: A culture of cooperation, trust, and transparency should befostered among researchers and developers of AI.
5) Race Avoidance: Teams developing AI systems should actively cooperate to avoid corner-cutting on safety standards.
Ethics and Values
6) Safety: AI systems should be safe and secure throughout their operationallifetime, and verifiably so where applicable and feasible.
7) Failure Transparency: If an AI system causes harm, it should be possible to ascertain why.
8) Judicial Transparency: Any involvement by an autonomous system in judicial decision-making should provide a satisfactory explanation auditable by a competent human authority.
9) Responsibility: Designers and builders of advanced AI systems are stakeholders in the moral implications of their use, misuse, and actions, with a responsibility and opportunity to shape those implications.
10) Value Alignment: Highly autonomous AI systems should be designed sothat their goals and behaviors can be assured to align with human values throughout their operation.
11) Human Values: AI systems should be designed and operated so as to becompatible with ideals of human dignity, rights, freedoms, and cultural diversity.
12) Personal Privacy: People should have the right to access, manage and control the data they generate, given AI systems’ power to analyze and utilizethat data.
13) Liberty and Privacy: The application of AI to personal data must not unreasonably curtail people’s real or perceived liberty.
14) Shared Benefit: AI technologies should benefit and empower as manypeople as possible.
15) Shared Prosperity: The economic prosperity created by AI should be sharedbroadly, to benefit all of humanity.
16) Human Control: Humans should choose how and whether to delegatedecisions to AI systems, to accomplish human-chosen objectives.
17) Non-subversion: The power conferred by control of highly advanced AI systems should respect and improve, rather than subvert, the social and civicprocesses on which the health of society depends.
18) AI Arms Race: An arms race in lethal autonomous weapons should beavoided.
Longer-term Issues
19) Capability Caution: There being no consensus, we should avoid strongassumptions regarding upper limits on future AI capabilities.
20) Importance: Advanced AI could represent a profound change in the historyof life on Earth, and should be planned for and managed with commensurate care and resources.
21) Risks: Risks posed by AI systems, especially catastrophic or existential risks, must be subject to planning and mitigation efforts commensurate with theirexpected impact.
22) Recursive Self-Improvement: AI systems designed to recursively self-improve or self-replicate in a manner that could lead to rapidly increasing qualityor quantity must be subject to strict safety and control measures.
23) Common Good: Superintelligence should only be developed in the service of widely shared ethical ideals, and for the benefit of all humanity rather than one state or organization.
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Taught by Winston Maxwell
Class one: 24 November 2020 Zoom link (https://telecom-paris.zoom.us/j/96831575375?pwd=R2liaTBrZ2JucHlYY2Q2OGI0TjlqQT09 ), 13:30 - 16:45. BY ZOOM ONLY
REQUIRED READING: Jack Balkin, The Three Laws of Robotics in the Age of Big Data
Introduction to classical ethics (IEEE guide to ethical design)
Plus video Shoshana Zuboff on "Surveillance Capitalism":: https://www.youtube.com/watch?v=8HzW5rzPUy8A short cartoon introducing the field of ethics
: https://www.youtube.com/watch?v=zPsoFhUDLuUClass two: 1 December 2020 (zoom link), 13:30 - 16:45. BY ZOOM ONLY
REQUIRED READING: (to be completed)
Activities: 3 -
Taught by Sophie Chabridon
Class three: 8 December 2020, 13:30 - 16:45.
Online using Big Blue Button at https://webconf.imt.fr/frontend/sop-kzr-h4d-vyh
Key issues covered:
- Definitions
- Sources of discrimination: bias in data, bias in algorithms
- Individual and group fairness
- State of the art solutions: data diversity, algorithm transparency
Class four: 15 December 2020, 13:30 - 16:45.
Online using Big Blue Button at https://webconf.imt.fr/frontend/sop-kzr-h4d-vyh
Presentations by students and discussion of research papers
Activities: 1 -
Taught by Fabian Suchanek
Class Five: Privacy and Data Security
5 January 2021, 13:30 - 16:45. The class takes place online here.
- protecting your data against loss
- protecting yourself against illicit account access
- protecting yourself against hackers
- protecting yourself against companies
Class Six: Account Security
12 January 2021, 13:30 - 16:45. The class takes place online here.
- protecting yourself against governments
- protecting yourself against microtargeting (optional material not covered in the lecture)
- Hackaton
The work done during the hackaton counts as your class contribution. The deadline is at the end of this lab! (If you cannot participate in the class, please let the lecturer know.)Activities: 0 -
Taught by Ada Diaconescu
Class seven: 19 January 2021, 13:30 - 16:45. Hybrid course: Télécom Paris Amphi 6, plus zoom link
Zoom information: Sujet : Data AI 951 AI Ethics
Heure : 19 janv. 2021 01:30 PM Paris
Participer à la réunion Zoom
https://telecom-paris.zoom.us/j/99540100235?pwd=b3puTm5pY3dvRDkrNzB4d3d1KzZtUT09
ID de réunion : 995 4010 0235
Code secret : 089545
Une seule touche sur l’appareil mobile
+33170372246,,99540100235#,,,,*089545# France
+33170379729,,99540100235#,,,,*089545# France
Composez un numéro en fonction de votre emplacement
+33 1 7037 2246 France
+33 1 7037 9729 France
+33 1 7095 0103 France
+33 1 7095 0350 France
+33 1 8699 5831 France
ID de réunion : 995 4010 0235
Code secret : 089545
Trouvez votre numéro local : https://telecom-paris.zoom.us/u/arTHaEM9AKey issues covered:
- Social side effects, including:
- relations
- society
- economy
- cognitive
- employment
Activities: 1 -
Class eight: 26 January 2021, 13:30 - 16:45.
Presentation of class posters. Please add your slide to the shared presentation here: https://partage.imt.fr/index.php/s/dzKiQEemeXqNLQo
Open the presentation in "ONLYoffice" on the partage platform, then copy/paste your content onto a new slide. The idea is to have a single presentation with 28 slides.
On Jan 26 I will divide you into 5 or 6 break-out groups, so that each of you can present your poster in 10 minutes in your break-out group. Each break out group will appoint a representative. Then we re-convene in the main plenary group, and each delegate will present a summary of the five presentations of the group.
ZOOM LINK:
https://telecom-paris.zoom.us/j/98100382118?pwd=N1o4UEtrY0hpOE92aTN1bFVLbmdwUT09
ID de réunion : 981 0038 2118
Activities: 1 -
2 February 2021, 13:30 - 16:45, final written exam will be held physically at Telecom Paris (room to be confirmed)
Written exam
The final exam will occur Tuesday Feb 2, 13h30 - 16h30 in Amphi Dieng Kuntz at Télécom Paris.There are 14 multiple choice questions, and 2 essay questions to be chosen out of a list of 7. The exam is designed to be completed in 2 hours (approx. 45 minutes for 14 multiple choice questions and 1h15 for the 2 essays), but you can take up to three yours if you want. The answer to each essay question should be between 1.5 and 2 pages handwritten. Do not consult internet or books. Remember to put your name on every sheet of paper!The questions are divided equally between my classes, Fabian Suchanek classes, Sophie Chabridon classes and Ada Diaconescu class. So take a look at the materials for all those classes this weekend so you’re not surprised by any question.Grading: 50% of points on written final exam, 50% of points based on class participation and posters
Activities: 0