The AI Ethics Boom: 150 Ethical AI Startups and Industry Trends

Motivation for Ethical AI Startups

  • Investors seek to assess AI risk as part of their comprehensive profiling of AI companies. EAIDB provides transparency on the players working to make AI safer and more responsible.
  • Internal risk and compliance teams need to operationalize, quantify and manage AI risk. Identifying a toolset to do so is critical. There is also an increasing demand for ethical AI practices, as identified in IBM Institute for Business Value’s report.
  • As regulators concretize policy around ethical AI practices, the companies on this list will only grow in salience. They fundamentally provide solutions to the problems AI has created.
  • On a more philosophical note, AI should work for everyone, not just one portion of the population. Enforcing fairness and transparency in black-box algorithms and opaque AI systems is of the utmost importance.

Category: Data for AI

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Trends and Dynamics

Category: ModelOps, Monitoring, and Observability

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Trends and Dynamics

Category: AI Audits and GRC

Description

Trends and Dynamics

Category: Targeted AI Solutions and Technologies

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Trends and Dynamics

Category: Open-Source Solutions

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Trends and Dynamics

Ecosystem Trends

  1. As policy is created, refined, and further defined, consulting firms may decline in popularity and MLOps / GRC platforms may rise due to the ability to programmatically enforce compliance given concrete metrics. This is counter to the trend over the last ten years in which consulting firms outpaced automated solutions.
  1. Incumbents in the space will start incorporating less effective versions of bias-related technology in an effort to keep their platforms viable in this new world of bias-conscious policy. They lack the specialty, expertise, and first-mover advantage of these ethical AI startups but have a well-established client base to draw from. Whether these companies can tap into new markets effectively remains to be seen.
  2. The modern “tech stack” will quickly evolve into an “ethics stack” in which the flow of data is carefully monitored, documented, and analyzed through products provided by companies in the aforementioned categories. For example, a company might employ Gretel for data privacy and synthetic data, Akira for MLOps management and continuous bias detection, then Saidot for model versioning and governance.
  3. The demand for ethical AI startups will perpetually increase because performing AI services correctly is domain-specific, context-specific, and very unstable (i.e. always needs to be monitored, checked for quality, etc.). There’s no “one-and-done” approach to AI. The “boom” for ethical AI is estimated to be somewhere from the mid-to-late-2020s and will follow a curve similar to “ethics predecessors” like cybersecurity in the late-2000s and privacy in the late-2010s. There will be a time in which policy, real case studies of AI gone wrong, and new discovery of biased AI (and a genuine desire to fix it) nudges companies in the right direction (through fear or will), leading to large demand and more inclusions in EAIDB.

Conclusion on Ethical AI Startups

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