Rayid Ghani

See: Text Mining Algorithm, co-EM Semi-Supervised Learning Algorithm, Data Science for Social Good, Machine Learning for Public Policy, Algorithmic Fairness, Responsible AI, Carnegie Mellon University.

References

2025-05-30

Deep Research https://chatgpt.com/s/dr_683999f21b408191bf49f56b5b061d1c
- Question: Profile Rayid Ghani, including his academic and professional roles, recent projects, publications, and contributions to ethical AI and data science for social good.
- Answer: Rayid Ghani is a U.S.-based computer scientist specializing in machine learning (ML) for social good and public policy. He currently serves as a Distinguished Career Professor in Carnegie Mellon University's Machine Learning Department (School of Computer Science) and Heinz College of Information Systems and Public Policy. Ghani is known for applying data science and AI to solve social challenges in areas like health, criminal justice, education, public safety, economic development, and urban infrastructure, with a focus on fair and equitable outcomes.
  - Current Academic Roles at Carnegie Mellon University:
    - Distinguished Career Professor: Ghani holds a joint faculty appointment in CMU's Machine Learning Department and Heinz College. In this role he bridges computer science and public policy, aiming to increase the use of AI/ML for social impact in government and non-profits. He joined CMU in 2019 (a return to his alma mater, where he earned his M.S. in Machine Learning in 2001) after leaving the University of Chicago.
    - Teaching and Mentorship: At CMU, Ghani teaches practical data science courses (e.g. Machine Learning for Public Policy Lab) that train students to tackle real-world policy problems with ML. He emphasizes interdisciplinary collaboration and ethics in these courses. (Previously, at U. Chicago he developed similar curricula on ML and public policy.)
    - Responsible AI Initiative Co-Lead: Ghani co-leads CMU's Responsible AI Initiative, a university-wide program (housed in the Block Center for Technology & Society) that convenes experts across disciplines to promote the ethical, accountable, and transparent use of AI. Launched in 2022, this initiative focuses on translating AI research into policy, building community partnerships, developing training on responsible AI, and ensuring AI advances "lead toward a more just and equitable world". Ghani moderated the initiative's kickoff panel and continues to guide its activities around AI policy and governance.
  - Major Initiatives and Leadership:
    - Data Science for Social Good (DSSG) Fellowship: One of Ghani's signature initiatives is the Eric & Wendy Schmidt Data Science for Social Good (DSSG) Summer Fellowship, which he founded in 2013 while at U. Chicago. The DSSG program is a full-time summer training fellowship that brings together aspiring data scientists to work on ML/AI projects with social impact – partnering with government agencies or NGOs to address issues such as public health, criminal justice reform, education, and economic inequality. Fellows receive hands-on experience applying data science to high-stakes problems, with guidance from mentors.
      - Expansion and Move to CMU: After launching at U. Chicago (and inspiring similar programs at UW, Stanford, Georgia Tech, Imperial College London, etc.), Ghani brought DSSG to Carnegie Mellon upon joining the faculty. Following a pandemic pause, the first CMU-based DSSG cohort (24 fellows) ran in summer 2022, completing six projects. The program continues annually at CMU, drawing a multidisciplinary cohort of students and young professionals from around the world.
      - Notable Projects: DSSG projects are developed with community partners and emphasize ethical, problem-driven design. For example, one 2022 DSSG team worked with the City of Baltimore to build an ML system that scans aerial images and city data to identify houses with damaged roofs, helping inspectors prioritize repairs in an equitable way across neighborhoods. Another team improved call routing for the 988 Suicide & Crisis Lifeline by predicting which crisis center is likely to pick up, potentially saving lives through reduced wait times. These projects illustrate Ghani's mission of using data science to deliver measurable social benefits at scale.
      - Focus on Ethics: Ghani has embedded ethics and fairness into DSSG's curriculum and project methodology. Teams are instructed to continually consider the societal impact of their models. "It's about consistently considering the ethical implications of every decision," Ghani says, rather than treating ethics as a one-off lecture. Participants must ask "What design choices are we making? What are the possible consequences of those choices downstream...?". This approach has made DSSG a model for integrating Fairness, Accountability, and Transparency (FAT) principles into applied data science training.
    - Coleridge Initiative (Data for Public Policy): In 2017, Ghani co-founded the Coleridge Initiative, a not-for-profit organization aimed at improving how government agencies use data for evidence-based policymaking. The Coleridge Initiative works with federal, state, and local governments to break down data silos and enhance analytical capacity for public decision-making. Ghani serves on its Board of Directors. This initiative reflects his commitment to building data infrastructure and training public sector analysts so that policy decisions can be informed by data and ML insights. (The Coleridge Initiative's projects include a secure data platform for researchers and "data challenges" that incentivize innovative policy analytics.)
    - Additional Leadership and Collaboration:
      - Center for Data Science & Public Policy: At U. Chicago, Ghani was the founding director of this center, which incubated many early "data-for-good projects" and developed open tools like Aequitas (an algorithmic bias audit toolkit, see below). This center served as a precursor to his later work at CMU bridging data science and governance.
      - Edgeflip: Ghani also co-founded Edgeflip, a social media analytics startup that spun out of the Obama 2012 campaign, which built tools to help nonprofits and advocacy groups leverage data for outreach. While smaller in scale, this venture demonstrated his interest in applying campaign-style data innovations to the nonprofit sector.
      - Advisory Roles: Ghani frequently advises governments and institutions on technology strategy. For instance, he has been a member of the National AI Advisory Committee (NAIAC) working groups and contributed expertise to the development of the U.S. National AI R&D "Roadmap" in 2023-2024. At CMU, he is affiliated with the Block Center for Tech & Society and other interdisciplinary efforts focused on public-interest technology.
  - Research and Publications (2020–2025): Rayid Ghani's recent research spans machine learning applications for social good as well as studies on algorithmic fairness and transparency.
    - Public Health & Policy Applications: "Validation of a Machine Learning Model to Predict Childhood Lead Poisoning" (JAMA Network Open, 2020) – Ghani and colleagues developed a predictive model to identify children at risk of lead poisoning, published in a top medical journal. Similarly, a 2020 study in IEEE AI for Good used time-series satellite imagery and ML to map informal settlements during the Venezuelan migration crisis, showcasing data science in humanitarian contexts.
    - Fairness in Machine Learning: "Empirical Observation of Negligible Fairness–Accuracy Trade-offs in ML for Public Policy" (Nature Machine Intelligence, 2021) – This influential study co-authored by Ghani found that in many real policy datasets, improving algorithmic fairness need not significantly sacrifice accuracy, countering the common assumption of an inherent trade-off. The work, based on experiments in areas like education and justice, suggests that well-designed ML models can be both fair and performant. Another paper compared bias mitigation methods on real high-stakes problems, guiding practitioners on effective techniques.
    - Transparency and Explainability: "Explainable Machine Learning for Public Policy: Use Cases, Gaps, and Research Directions" (Data & Policy, 2023) – Ghani and co-authors surveyed how explainable AI methods can support decision-making in government, identifying current gaps and proposing a research agenda to make ML more interpretable for public sector uses. This work aligns with his broader push for transparency in AI systems deployed in social programs.
    - Fairness Tools and Guidelines: Ghani has contributed to practical tools like Aequitas, an open-source bias audit toolkit developed at U. Chicago's Center for Data Science & Public Policy. Aequitas provides data scientists and policymakers a way to measure and mitigate bias in ML models (e.g. checking if a predictive model is fair across race or gender groups). In 2023, he co-presented a tutorial on bias and fairness in ML at KDD 2023, sharing hands-on guidance for practitioners. He also co-authored a 2023 paper on "pipeline-aware ML fairness" (ACM EAAMO 2023) that calls for considering fairness at each stage of the ML lifecycle.
    - Data Science Education: Emphasizing the importance of practice-based learning, Ghani wrote "Taking our Medicine: Standardizing Data Science Education with Practice at the Core" (Harvard Data Science Review, 2021), advocating for curricula that integrate real problem-solving experiences. This reflects his approach in programs like DSSG.
  - Contributions to Fairness, Accountability, and Transparency in AI: A core theme of Ghani's work is ensuring that AI systems are fair, accountable, and transparent (FAccT), especially when used in government or high-impact social domains:
    - Fairness Research: Ghani's research provides evidence-based insights that challenge the narrative of "accuracy vs. fairness." His 2021 Nature Machine Intelligence paper demonstrated that with careful model design and feature selection, algorithms for social outcomes (like allocating healthcare or social services) can often improve fairness metrics with minimal loss in accuracy. By highlighting real-world cases with "negligible fairness–accuracy trade-offs," this work encourages policymakers to demand both fairness and efficacy from AI tools.
    - Bias Audit Tools: Through the development of Aequitas, Ghani has equipped the field with a practical toolkit to audit algorithms for bias. Aequitas (available as a Python library and web app) enables organizations to generate bias reports, examining metrics like disparate impact or false positive rates across demographic groups. By open-sourcing this tool, Ghani has helped countless data teams assess and improve the fairness of predictive models in domains from criminal justice risk assessments to credit lending.
    - Transparency & Explainability: In his roles, Ghani advocates for making AI decision processes understandable. He stresses that deploying ML in public policy requires not just accuracy but also interpretability so that officials and the public can trust and scrutinize model outputs. His 2023 Data & Policy paper mapped out how explainable ML techniques (like interpretable models or post-hoc explanations) can be applied in areas such as child welfare, policing, and health, and what research is needed to fill gaps. This contributes to the broader FAccT goal of algorithmic transparency.
    - Accountability in Government AI: Ghani is actively involved in advising how governments procure and regulate AI. In September 2023, he testified as an expert witness before the U.S. Senate in a hearing on "Governing AI Through Acquisition and Procurement." He argued for mechanisms to ensure AI systems acquired by government are thoroughly vetted for bias, security, and effectiveness. Later that month he joined colleagues in briefing bipartisan Congressional groups on AI governance and accountability, helping lawmakers understand the importance of oversight and evaluation in public-sector AI use. These contributions show Ghani's commitment to making AI accountable to the public interest, not just technically performant.
    - Ethical AI Advocacy: Within academia and industry forums, Ghani frequently speaks on ethical AI. He co-leads CMU's Responsible AI initiative (as noted) to institutionalize ethical considerations in research and innovation. He also served on a National Academies of Sciences committee examining algorithmic bias, co-authoring an article in Daedalus (2024) on the societal impacts of AI bias. Across these efforts, Ghani emphasizes multidisciplinary collaboration – bringing social scientists, legal experts, and affected communities into the AI design loop to ensure fairness and justice.
  - Media Engagement and Public Speaking (2022–2025): Rayid Ghani's expertise in AI and social good has made him a sought-after voice in both media and policy discussions in recent years:
    - Expert Commentary: He is regularly quoted in prominent media outlets on topics of AI impacts, ethics, and policy. For example, National Geographic interviewed Ghani in 2024 for a feature answering common AI questions; he explained how generative AI models prioritize statistical probability over truth, and why it's hard for people to trust AI errors without better transparency. He has also been cited in articles by The Guardian and Wired in 2023 during coverage of high-profile AI events (such as the OpenAI leadership controversy), providing insight into AI governance and the risks of unregulated development. In an ABC News segment (Sept. 2023) on AI in higher education, Ghani discussed how academia is adapting to generative AI tools, reflecting on the need to guide students in using AI responsibly. These appearances underscore his role as a leading public educator on AI, able to translate technical issues for general audiences.
    - Public Speaking and Panels: Beyond news media, Ghani frequently speaks at conferences, think tanks, and government briefings. In addition to his Senate testimony noted above, he has given keynote talks on data science in government and has been featured in AI policy panels. For instance, he moderated a panel on Responsible AI at CMU's launch event (April 2022) with participants from the White House and industry. He was also highlighted by the Pittsburgh Business Times as one of "20 People to Know in Higher Education" in 2023, which cited his work at CMU and influence in shaping tech policy. Ghani's ability to communicate the value and pitfalls of AI to non-technical audiences has made him a valuable voice in public discourse around technology.
    - Interviews and Thought Leadership: Ghani serves on the board of the AI for Good Foundation and has been interviewed about his vision for socially beneficial AI. In a 2020 AI for Good interview, he noted that "Machine learning and AI give you one additional set of tools… to help you tackle these problems," emphasizing the need to integrate ML with domain expertise to solve societal challenges. He often shares "success stories" of data-for-good projects to inspire other data scientists. Moreover, Ghani maintains an active online presence (Twitter/X, personal blog) where he disseminates lessons from projects and engages with the data science community on issues of equity and impact.
- Citations:

[1] https://technical.ly/civic-news/cmu-rayid-ghani-testimony-senate-hearing-ai/
[2] https://engineering.cmu.edu/directory/bios/ghani-rayid.html
[3] https://en.wikipedia.org/wiki/Rayid_Ghani
[4] https://theorg.com/org/coleridge-initiative/org-chart/rayid-ghani
[5] https://www.rayidghani.com/
[6] https://technical.ly/software-development/cmu-responsible-ai-initiative/
[7] https://www.cmu.edu/block-center/responsible-ai/index.html
[8] https://www.cmu.edu/news/stories/archives/2023/february/making-meaningful-impact-using-data-science-for-social-good
[9] https://coleridgeinitiative.org/about-board-of-directors
[10] https://medium.com/@emehrotr/know-if-your-ml-model-is-the-champion-of-justice-using-aequitas-c78691c76c37
[11] https://www.cmu.edu/news/stories/archives/2024/may/cmu-experts-lent-expertise-to-new-us-artificial-intelligence-roadmap
[12] https://www.rayidghani.com/publications/
[13] https://dblp.org/pid/19/6687
[14] https://dssg.github.io/fairness_tutorial/
[15] https://www.nationalgeographic.com/science/article/ai-effects-questions-concerns
[16] https://www.heinz.cmu.edu/faculty-research/profiles/ghani-rayid
[17] https://www.bizjournals.com/pittsburgh/news/2023/02/27/20-people-to-know-education-rayid-ghani.html
[18] https://ai4good.org/blog/interview-with-rayid-ghani/

2003

(Jones, Ghani et al., 2003) ⇒ Rosia Jones, Rayid Ghani, Tom Mitchell, and Elleen Riloff. (2003). “Active Learning for Information Extraction with Multiple View Feature Sets.” In: Proceedings of the International Workshop & Tutorial on Adaptive Text Extraction and Mining held in conjunction with the 14th European Conference on Machine Learning and the 7th European Conference on Principles and Practice of.

2002

(Yang et al., 2002) ⇒ Yiming Yang, Seán Slattery, and Rayid Ghani, (2002). “A Study of Approaches to Hypertext Categorization.” In: Journal of Intelligent Information Systems, 18(2-3). ]doi:10.1023/A:1013685612819

2000

(Nigam & Ghani, 2000) ⇒ Kamal Nigam, and Rayid Ghani. (2000). “Analyzing the Effectiveness and Applicability of Co-training.” In: Proceedings of the ninth International Conference on Information and knowledge management (CIKM 2000). doi:10.1145/354756.354805
(Ghani et al., 2000) ⇒ Rayid Ghani, Rosie Jones, Dunja Mladenić, Kamal Nigam, and Sean Slattery. (2000). “Data Mining on Symbolic Knowledge Extracted from the Web.” In: Proceedings of the KDD-2000 Workshop on Text Mining.
(Jones & Ghani, 2000) ⇒ Rosie Jones, and Rayid Ghani. (2000). “Automatically Building a Corpus for a Minority Language from the Web. In: Proceedings of the Student Workshop of the 38th Annual Meeting of the Association for ...

Rayid Ghani

References

2025-05-30

2012

2007

2006

2003

2002

2000

Navigation menu

Search