Schedule (10-735/80-831)

	A	B	C	D	E	F	G	H	I	J	K
1	Weekday	Date	Session	Module	Topics	Primary Lecturer	References & Reading Material	Assessments	Lecture Note Scribing (Name \| AndrewID)	Received scribe?
2	Tue	Jan-16	1	Introduction	Examples of AI for social good what can go wrong; course overview & logistics	AJL & HH	AI for Social Good AI4People—An ethical framework for a good AI society Weapons of Math Destruction Dark Patterns: Past Present and Future On Being a Data Skeptic		N/A
3	Thur	Jan-18	2	Introduction	What is AI? The ML pipeline & lifecycle	HH	Everyone wants to do the model work, not the data work Problem Formulation and Fairness Measurement and Fairness Algorithmic Bias in Autonomous Systems A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle		Brook Russi Arias (brussiar)	Y
4	Tue	Jan-23	3	Introduction	Class activity	HH	Pipeline cards		N/A
5	Thu	Jan-25	4	Introduction	Ethical and responsible AI principles and operationalization	AJL	The global landscape of AI ethics guidelines The growing ubiquity of algorithms in society: implications, impacts and innovations Critiquing the Reasons for Making Artifcial Moral Agents From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices		Jocelyn Tseng (jocelynt)	Y
6	Tue	Jan-30	5	Introduction	Benefit, Assistance, and Failure Modes	AJL	Beneficent Intelligence: A Capability Approach to Modeling Benefit, Assistance, and Associated Moral Failures through AI Systems Women and equality: the capabilities approach Capability Sensitive Design for Health and Wellbeing Technologies	Quiz 1 (introduction)	Anoushka Shrivastava (anoushk2)	Y
7	Thu	Feb-01	6	Justice & Fairness	Introduction to fairness & justice	AJL	Disparate Impact in Big Data Fairness through awareness Local Justice "We are all Different": Statistical Discrimination and the Right to be Treated as an Individual	Project descriptions released.	Shurui Cao (shuruic)	Y
8	Tue	Feb-06	7	Justice & Fairness	Definitions and impossibility results	HH	Inherent Trade-Offs in the Fair Determination of Risk Scores A Moral Framework for Understanding Fair ML Through Economic Models of Equality of Opportunity		Mahika Varma (mahikav), Yashika Batra (ybatra)	Y
9	Thu	Feb-08	8	Justice & Fairness	Causal notions of fairness	HH	Counterfactual Fairness Counterfactual Risk Assessments, Evaluation, and Fairness	HW 1 released.	Ida Mattsson (imattsso)	Y
10	Tue	Feb-13	9	Justice & Fairness	Debiasing and Fairness Interventions	HH	Hidden in Plain Sight—Reconsidering the Use of Race Correction in Clinical Algorithms
11	Thu	Feb-15	10	Justice & Fairness	Structural Injustice & rectification	AJL	Nozick, Robert. Anarchy, State, and Utopia Ch 7: Distributive Justice. Rawls, John. Justice as Rational Choice Behind a Veil of Ignorance		Diana Gomez (dggomez), Divya Kartik (dkartik)	Y
12	Tue	Feb-20	11	Justice & Fairness	Class activity	HH		Quiz 2 (Justice and Fairness)	N/A
13	Thu	Feb-22	12	Transparency & Explainability	An overview of explanations	HH	Explanation in Artificial Intelligence: Insights from the Social Sciences Towards A Rigorous Science of Interpretable Machine Learning Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning	HW 1 due.	Ethan Cheong (echeong)	Y
14	Tue	Feb-27	13	Transparency & Explainability	Methods and metrics	HH	Why Should I Trust You? Explaining the Predictions of Any Classifier Algorithmic Transparency via Quantitative Input Influence Counterfactual Explanations without Opening the Black Box		Ronit Batchu (rbatchu)	Y
15	Thu	Feb-29	14	Transparency & Explainability	Pittfalls and remedies	AJL	Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead Artificial intelligence and Black-Box Medical Decisions: Accuracy vs Explainability The Mythos of Model Interpretability Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation	HW2 released. Quiz 3 (Transparency and explainability)	Will Flanagan (wwflanag)	Y	under review
16	Tue	Mar-05	No Class; Spring Break
17	Thu	Mar-07	No Class; Spring Break
18	Tue	Mar-12	15	Privacy & Surveillance	Introduction to privacy & surveillance	AJL	Privacy and human behavior in the age of information Privacy, poverty, and big data: A matrix of vulnerabilities for poor Americans Big Data and Due Process: Toward a Framework to Redress Predictive Privacy Harms		Anvi Joshi (anvij)
19	Thu	Mar-14	16	Privacy & Surveillance	Contextual integrity	AJL	Privacy in Context: Technology, Policy, and the Integrity of Social Life		Medha Palavalli (mpalaval)	Y
20	Tue	Mar-19	17	Privacy & Surveillance	Differential privacy	HH	Differential privacy: A survey of results No Free Lunch in Data Privacy	HW2 due. Quiz 4 (Privacy & surveillance).	Zhicheng Zhang (zhichen3)
21	Thu	Mar-21	18	Validity & Reliability	Validity in social sciences vs. in ML	HH	A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithms	Project check-in with the instructors.	Yuanxin Zhu(yuanxin3)
22	Tue	Mar-26	19	Validity & Reliability	Class activity	HH		Quiz 5 (Validity and Reliability)	N/A
23	Thu	Mar-28	20	Safety and Long-term Impacts	Gen AI risks and harms	AJL	Ethical and social risks of harm from Language Models [Economic impact and future of work]		Zhihan Li (zhihanl3)
24	Tue	Apr-02	21	Safety and Long-term Impacts	Gen AI evaluations	AJL	Sparks of Artificial General Intelligence: Early experiments with GPT-4 Can LLMs Really Reason and Plan	Quiz 6 (Safety and long-termism)	David Luo (djluo)
25	Thu	Apr-04	22	Accountability & Governance	Definitions	HH	What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability		Vanessa Colon (vcolon)
26	Tue	Apr-09	23	Accountability & Governance	Tools and processes	HH	Datasheets for Datasets Model cards for model reporting FactSheets: Increasing Trust in AI Services through Supplier’s Declarations of Conformity		Gao Mo (gaom)
27	Thu	Apr-11	No Class; Spring Carnival
28	Tue	Apr-16	24	Accountability & Governance	Impact assessment and failures of imagination	AJL	Algorithmic Impact Assessments and Accountability: The Co-construction of Impacts		Priyanshi Garg (pgarg2)	Y	under review
29	Thu	Apr-18	25	Accountability & Governance	Class activity	HH		Quiz 7 (Accountability & governance)	N/A
30	Tue	Apr-23	26	Conclusion	Holistic analysis of a use-case: AI for medical diagnosis	AJL	Roberts et al., Common Pitfalls and recommednations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Oakden-Rayner et al. Hidden Stratification Causes Clinically Meaningful Failures in Machine Leaning for Medical Imaging.		Deepti Murthy (dmurthy)
31	Thu	Apr-25	27	Conclusion	Key takeaways from the course Where is the field headed?	AJL	Roles for Computing in Social Change Four Years of FAccT: A Reflexive, Mixed-Methods Analysis of Research Contributions, Shortcomings, and Future Prospects	Project reports due.
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