Introduction

AI bias is an unintentional underlying prejudice in data and algorithms that results in prejudices and unwanted discrimination. At a time when bias in AI has become one of the hottest debated topics, the playbook by The Center for Equity, Gender and Leadership at the Haas School of Business (University of California, Berkeley) outlines top-line information on bias in AI and the necessary steps that can be taken to address the bias. The end objective of the playbook is to mitigate bias in AI and unlock its true value in a responsible and equitable manner.

The playbook opens by stating an interesting fact: AI could contribute $15.7 trillion to the global economy by 2030. However, it quickly adds that bias in AI can restrict the full potential as it unfairly allocates opportunities and provides results that are discriminatory and inaccurate. The results may negatively impact a person’s well-being which in turn may cost businesses their reputation and trust.

The need to understand and mitigate bias in AI is being driven by various stakeholders that include academia, government, multilateral institutions and NGOs. The first step, however, is trying to understand the reason behind bias. AI algorithms are biased mainly because they are created by humans, who themselves are biased, albeit unwittingly. The creators of such algorithms may fail to integrate fair and ethical values into the system which ultimately affects the end product. The other possibilities are inadequate methods of data collection, generation and data labelling.

Having given a brief overview of the causes of bias and its impact on society and businesses, the report steers its way to identify the challenges often faced in the process of bias mitigation. The challenges have been categorized into organizational levels, industry-wide levels and societal levels and includes limitations such as lack of domain knowledge and accountability, lack of regulations and actionable guidance, persistence of black box algorithms and outdated education approaches for data and scientists.


Relevance of the Report

As the use of AI is increasing over time with senior business decision-makers planning to either deploy or ramp up deployment, investment in AI is also steadily accelerating. The playbook will come in handy for C-level executives who would want to know ways and means to address biases in AI. It is unique in its own way as it draws examples from academic literature and opinions of experts across disciplines and based on the information charts out seven strategic plays that may help business leaders address the issue of biasedness.

 

Key Takeaways

  • A snapshot view of strategic plays that can address AI bias that often impacts business. The plays have been categorized into Teams, AI Models and Corporate Governance & leadership
  • Teams include enabling diverse and multi-disciplinary teams working on algorithms and AI systems and promoting a culture of ethics and responsibility related to AI.
  • Responsible dataset development and establishing policies culture of ethics and responsibility related to AI are the main plays under AI models
  • Corporate governance and leadership focus on engaging CSR to advance responsible / ethical AI and larger systems change or using voice and influence to advance industry change and regulations for responsible AI

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DISCLAIMER

The information provided on this page has been procured through secondary sources. In case you would like to suggest any update, please write to us at support.ai@mail.nasscom.in