Machine Learning and Human Capital Complementarities: Experimental Evidence on Bias Mitigation

Prithwiraj  Choudhury; Evan  Starr; Rajshree  Agarwal

Article | Strategic Management Journal | August 2020

Machine Learning and Human Capital Complementarities: Experimental Evidence on Bias Mitigation

by Prithwiraj Choudhury, Evan Starr and Rajshree Agarwal

Abstract

The use of machine learning (ML) for productivity in the knowledge economy requires considerations of important biases that may arise from ML predictions. We define a new source of bias related to incompleteness in real time inputs, which may result from strategic behavior by agents. We theorize that domain expertise of users can complement ML by mitigating this bias. Our observational and experimental analyses in the patent examination context support this conjecture. In the face of “input incompleteness,” we find ML is biased towards finding prior art textually similar to focal claims, and domain expertise is needed to find the most relevant prior art. We also document the importance of vintage-specific skills and discuss the implications for artificial intelligence and strategic management of human capital.

Keywords: Machine learning; bias; Human Capital; Management; Strategy;

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Choudhury, Prithwiraj, Evan Starr, and Rajshree Agarwal. "Machine Learning and Human Capital Complementarities: Experimental Evidence on Bias Mitigation." Strategic Management Journal 41, no. 8 (August 2020): 1381–1411.

This paper argues that intra-firm geographic mobility is an understudied mechanism that can help mitigate coordination failures in a geographically distributed organization. The paper presents an organizing framework on how intra-firm geographic mobility creates value for firms and discusses how intra-firm geographic mobility can create value for individual workers. The paper concludes by presenting a future research agenda for intra-firm geographic mobility in light of emerging phenomena such as global collaborative patenting by multinationals, temporary colocation of knowledge workers, and nonstandard work.

Although the COVID-19 crisis has halted travel in recent months, geographic mobility has become critical for managers and knowledge workers hoping to advance in today’s globalized economy, and that trend is unlikely to reverse. Geographic mobility can pay off financially and can boost your career by improving your problem-solving and leadership skills and building your networks. Yet there are constraints and costs to relocating and I preview the ROPE framework of mobility frictions, outlining the regulatory/occupational constraints and personal/economic costs that lead to immobility. Anyone contemplating a geographic move should think through its full implications first. Research on knowledge workers in a variety of organizations around the world—from Indian bureaucrats to American consultants—suggests some common principles for getting the most out of relocations: (1) Make moves early in your career, when frictions are usually lower and you can apply the learning over many more years of work. (2) Step out of your comfort zone to develop problem solving skills. (3) Find creative workarounds for constraints. (4) To minimize the psychological costs, find ways to stay connected to home. (5) Time your trips to HQ strategically, and plan the next step right from the start.

Case | HBS Case Collection | July 2020

Super 30: Educating the Elite Poor

Prithwiraj Choudhury, Tarun Khanna and Shreya Ramachandran

In the summer of 2019 in New Delhi, S K Shahi and his daughter, Meenakshi, faced a difficult problem. India had 19 centers of their non-profit, the Center for Social Responsibility and Leadership. Also called the 'Super 30' program, this offered free training for India's rigorous engineering entrance exam, the Joint Entrance Examination, to 30 - or sometimes 50 or 100 - high school graduates, selected by merit. Shahi and Meenakshi ran these centers using the corporate social responsibility funds donated by large state-owned companies in India, and some private companies. They had been operational for over a decade, with alumni working in academia, top tech companies, and education, having graduated from India's best colleges. They now had a difficult decision to make: should they expand in large metropolis cities - New Delhi; Kanpur; Mumbai - that would attract talented youth from all over India? Or else, given India's remote geographies and uneven distribution of income and educational benefits, was it best to expand to these tough regions to help those who most needed it? An additional source of concern was the operational and logistical cost of running centers in different parts of the country. What was the best way forward: city or town, urban or remote? New Delhi or Kashmir?

Keywords: Education; Higher Education; Diversity; Nonprofit Organizations; Operations; Expansion; Geographic Location; Strategy; Decision Making; India;

Citation:

Choudhury, Prithwiraj, Tarun Khanna, and Shreya Ramachandran. "Super 30: Educating the Elite Poor." Harvard Business School Case 621-004, July 2020. View Details

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Machine Learning and Human Capital Complementarities: Experimental Evidence on Bias Mitigation

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