Artificial intelligence (AI) and machine learning (ML) are becoming increasingly prevalent in modern organizations. While these technologies offer numerous benefits, including increased efficiency and cost savings, they also introduce new governance challenges. In this blog post, we will compare governance in the world of AI and ML.
Data Quality
Data quality is paramount for any AI or ML project. The accuracy and reliability of these technologies rely on quality data. Governance policies must ensure that data is high-quality, accurate, and up-to-date. According to a study by IBM, poor data quality costs US companies an average of $3.1 trillion per year. This highlights the importance of data quality in AI and ML governance.
Bias and Fairness
AI and ML algorithms learn from data, and this data can carry bias. Bias leads to unfair and inaccurate predictions, which can have significant consequences. Governance policies must ensure that algorithms are unbiased and fair. A study by the National Bureau of Economic Research found that AI-powered hiring tools were biased against women. A lack of governance and regulations can lead to such unfair outcomes.
Explainability and Transparency
AI and ML algorithms can be complex and difficult for non-experts to understand. This lack of transparency can lead to mistrust and hinder adoption. Governance policies must ensure that algorithms are explainable and transparent. This includes documenting the data used, the algorithms used, and the outcomes produced. Additionally, any decisions made by these algorithms must be explainable.
Security and Privacy
AI and ML projects rely on vast amounts of data. This data must be secured, and users' privacy must be protected. Governance policies must ensure that security and privacy are at the forefront of any AI or ML project. This includes data protection, user consent, and compliance with relevant regulations.
Conclusion
Governance is essential in the world of AI and ML. Data quality, bias and fairness, explainability and transparency, and security and privacy are critical factors that must be addressed. Successful governance can help organizations reap the benefits of these technologies while avoiding unintended consequences.
References
- IBM. (n.d.). Data quality: The costly consequences of bad data. Retrieved from https://www.ibm.com/analytics/data-quality
- National Bureau of Economic Research. (2018). Hiring algorithms and bias: A case study. Retrieved from https://www.nber.org/papers/w24969.pdf