Machine learning for science and society

By Cynthia Rudin, Kiri L. Wagstaff. 2013

119 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, AI Ethics, Fairness & Society, Social Sciences & Humanities

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Lesia Semenova
2. Chudi Zhong
3. Zhi Chen
4. Haiyang Huang
5. Kush R. Varshney
6. Cynthia Rudin
7. Alex B. McBratney
8. Budiman Minasny

In Management Science and Operations Research

1. Chudi Zhong
2. Lesia Semenova
3. Zhi Chen
4. Haiyang Huang
5. Kush R. Varshney
6. Carla P. Gomes
7. Fernando Diaz
8. Carlos Castillo

In AI Ethics, Fairness & Society

1. Alexandra Olteanu
2. Lesia Semenova
3. Chudi Zhong
4. Zhi Chen
5. Haiyang Huang
6. Kush R. Varshney
7. Carla P. Gomes
8. Fernando Diaz

In Social Sciences & Humanities

1. Carla P. Gomes
2. Kush R. Varshney
3. Fernando Diaz
4. Carlos Castillo
5. Alexandra Olteanu
6. Cynthia Rudin

Citations in the corpus

• Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, background): “It is universally agreed that interpretability is a key element of trust for AI models (Wagstaff, 2012; Rudin and Wagstaff, 2014; Lo Piano, 2020; Ashoori and Weisz, 2019; Thiebes et al., 2020; Spiegelhalter, 2020; Brundage et al., 2020).”
• Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Wagstaff (2012) and Rudin and Wagstaff (2014) raise concerns about the pervasiveness of abstract metrics, such as precision and recall, which explicitly ignore relevant domain-specific ones.”
• Machine learning and soil sciences: a review aided by machine learning tools (2019, background): “Nevertheless, many would find that ML methods can help in the scientific process (Mjolsness and DeCoste, 2001; Rudin and Wagstaff, 2014): observations, empirical and theory-based model development, and simulations of soil processes (Rossiter, 2018).”
• Data Science of the People , for the People , by the People : A Viewpoint on an Emerging Dichotomy (2015, background): “As has also been recognized by others, we believe the primary reason for this emerging dichotomy is the recognition that to make impact in certain applications of data science, the accuracy of the results is not the only criterion that matters [53, 42].”
• Computational Sustainability (2011, background): “, [10, 12, 14, 22, 28, 30, 34]), such computational expertise is far from the critical mass required to address the formidable societal and sustainability challenges that we face today.”
• A survey of machine learning for big data processing (2016, background): “implementation in a wide range of applications, machine learning has covered almost every scientific domain, which has brought great impact on the science and society [19].”