Introduction

The Ethical AI Database (EAIDB) is a curated collection of startups that are either actively trying to solve problems that AI and data have created or are building methods to unite AI and society in a safe and responsible manner.

2022 was a big year for our recognized startups. We witnessed multiple strategic acquisitions, lots of dynamic movement within each category, and a ton of investment inflow. EAIDB was founded in Q2 2022 with 148 companies, but since then we have grown by about 45% and now have 215 verified companies. We continue to capture more of the space every day. View our growing market map here.

This report is a conglomeration of insights we have gained throughout the course of 2022. We cover market movements within each of our five categories, highlights for the year, and more. EAIDB publishes reports and market maps on a semiannual basis (switching away from quarterly reports to provide more holistic and interesting analyses).

Additions & Deletions

We welcome 25 new firms to the ethical AI ecosystem. The database now contains 216 companies actively working to better the way we use artificial intelligence and machine learning in practice. Welcome to EAIDB!

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We have also identified some companies in EAIDB that we feel no longer fit our criteria upon closer inspection. Some are simply too matured (we have a strong preference for startups that are fairly early on in their journey, Series E or earlier), some are inactive, and some we feel do not directly relate to AI ethics or its many subcategories. As we continue to refine our criteria, EAIDB will always be transparent in the changes made to its map and constituents. The following companies were removed from the database:

The distribution of companies in EAIDB is skewed heavily towards vertical solutions, primarily because of the more mature hiretech and fintech markets. The other categories are just as relevant, however, and tackle different aspects of the AI pipeline.

Funding Activity

Ethical AI startups raised over $1.1bn in 2022 from 55 companies. The most common round was a Series A investment.

Company Amount (millions USD) Round Lead Investors
Mission Control 2.0 Pre-Seed Stage Venture Partners
QuantPi 2.7 Pre-Seed Capnamic Ventures
Veil AI 1.3 Pre-Seed BioInnovation Institute
Anch AI 2.1 Seed Benhamou Global Ventures
Black AI 5.4 Seed Jelix Ventures
Checkstep 5.0 Seed Dawn Capital, Form Ventures
Datatron 12 Seed Undisclosed
Deeploy 2.2 Seed Curiosity VC
Fairgen 2.5 Seed Tal Ventures
Giskard AI 1.7 Seed Elaia
HiddenLayer 6.0 Seed Ten Eleven Ventures
Pendella 5.2 Seed American Family Ventures, MassMutual Ventures
Privya AI 6.0 Seed Hyperwise Ventures
Protopia AI 6.0 Seed ATX Venture Partners
Sapia 7.0 Seed Hudson
Sarus Undisclosed Seed Undisclosed
Alva Labs 13 Series A VNV Global
Aporia 25 Series A Tiger Global Management
Bodyguard AI 11 Series A Keen Venture Partners, Ring Capital
Brighter AI Undisclosed Series A Armilar Venture Partners
CausaLens 53 Series A Molten Ventures, Dorilton Capital
Credo AI 13 Series A Sands Capital Ventures
Diversio 6.3 Series A First Round Capital
FairPlay AI 10 Series A Nyca Partners
Mathison 25 Series A F-Prime Capital
Modulate AI 30 Series A Lakestar
Netacea 11 Series A Undisclosed
Private AI 8.0 Series A BDC Venture Capital
Reejig Undisclosed Undisclosed Salesforce Ventures
Reejig 10.5 Series A Undisclosed
Sapia 11.8 Series A Macquarie Partners, W23
Synthesis AI 17 Series A 468 Capital
X0PA AI 4.2 Series A ICCP Venture Partners
Arize AI 38 Series B TCV
Arthur AI 42 Series B Acrew Capital, Greycroft
Cohere 122 Series B Tiger Global Management
Datagen 50 Series B Scale Venture Partners
Mostly AI 25 Series B Molten Ventures
Spectrum Labs 32 Series B Intel Capital
TruEra 25 Series B Menlo Ventures
Flock Safety 150 Series E Tiger Global Management
MDClone 63 Series C Viola Growth, Warburg Pincus
Pave 100 Series C Index Ventures
Ambient AI 52 Undisclosed Andreessen Horowitz
Enzai Technologies 0.7 Undisclosed Techstart Ventures
Equalture 2.0 Undisclosed Shoe Investments
Eskalera Undisclosed Undisclosed Ulu Ventures
Inrupt Undisclosed Undisclosed Accenture Ventures
Mindtech Global 3.7 Undisclosed Appen
Pipeline Equity Undisclosed Undisclosed Workday Ventures
Prifina 0.3 Undisclosed Undisclosed
QuadFi 99 Debt Financing Crayhill Capital Management
Synthesized 2.8 Undisclosed Deutsche Bank
Textio 1.0 Undisclosed Undisclosed
Troj AI 2.3 Undisclosed Build Ventures, Flying Fish Partners
vAIsual Undisclosed Undisclosed Individual Investors
Acquisitions & Exits

There were three separate acquisitions made by companies in EAIDB. All three were made by hiretech companies (Crosschq, Pave, Alva Labs) on the back of their most recent funding rounds.

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Crosschq acq. TalentWall

Crosschq (candidate insight and people analytics platform with DE&I tracking) acquired TalentWall (data-driven recruiting analytics platform) following their $30m Series A funding round.

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Pave acq. Advanced-HR

Pave (compensation benchmarking) acquired Advanced-HR (similar platform) following their $100m Series C funding round. They have integrated Advanced-HR's "Option Impact" product into their own offering.

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Alva Labs acq. DevSkills

Alva Labs (candidate assessment platform) acquired DevSkills (online coding skills testing platform) in order to provide their highly customizable coding tests through their platform.

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There were also four exits within EAIDB's ecosystem. Again, most of these were hiretech related (since fair hiretech is the most mature of all verticals in the ethical AI ecosystem). Since the "hiretech boom" of the mid-2010s, this area has been gaining momentum and is finally beginning to show investors some dividends.

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Paycor acq. Talenya

Paycor acquires Talenya to integrate their AI-based recruiting software to their existing human capital management (HCM) platform. Talenya, an EAIDB startup, is a powerful tool for tracking and executing companies' DE&I strategies.

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Silverback United acq. Headstart AI

In another hiretech acquisition, Silverback United acquires Headstart AI, an automated hiring platform to perpetuate their strategy of building a portfolio of vertically-specific, high data-value companies.

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Reddit acq. Oterlu AI

Reddit acquihires Oterlu AI to bolster their content moderation team. Oterlu provides automated content moderation technology, but Reddit was after the talent within the Oterlu team to apply their thought leadership to Reddit's existing technology.

Read PR
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Anonos acq. Statice GmbH

The only non-hiretech acquisition within EAIDB's universe in 2022, Anonos (data privacy / security) acquired Statice (synthetic data platform). Statice's technology will be integrated into Anonos' "Data Embassy" product.

Read PR
New Technology & Events

There was a ton of innovation in 2022 from the ethical AI ecosystem. From the launch of new companies to the introduction of groundbreaking technologies, the ecosystem has never looked so impressive. Here are a few highlights from the year.

Neurosymbolic AI is the future. Umnai (founded in 2019), a leading researcher and technology provider of neurosymbolic AI, has developed technology that significantly improves existing ML methodologies with inherently auditable, interpretable, and faster models. The company gave several presentations in 2022, including at AI Forum. Read more about their technology here.

Integrate AI has developed a seamless way to perform federated learning. Federated learning provides strong privacy guarantees because it is a methodology that never allows data to leave its silo. Integrate has created developer-friendly APIs to access their technology and launched their federated learning platform in 2022.

Superwise operationalizes model observability. Their Model Observability platform was made available on the Datadog marketplace in March 2022. This is essentially a marker of product differentiation and tried-and-true product quality.

Ethically Aligned AI is a consulting / educational firm. In 2022, they developed a microcredential named "Artificial Intelligence Ethics" with Canada's Athabasca University. More information can be found here.

FairPlay AI debiases lending algorithms to produce better, more fair outcomes. Their "Mortgage Fairness Report" on the state of mortgage fairness in the United States was released in late 2022. More information can be found here.

Cangrade focuses on bias reduction in hiring decisions. In 2022, the company was granted a patent on their particular method of bias removal via a separate "Adverse Impact" test that is applicable outside of just hiring. Read more here.

QuadFi is a lending platform with support for thin or no files. In 2022, the company released a product for global immigrants with little to no credit history (and does not rely on FICO scores). Read more here.

Aporia is a model monitoring and observability company. Last year, they launched their novel Direct Data Connectors (DDC) service, which allows one to directly connect to training and inference datasets to essentially be able to monitor predictions without sampling, production code changes, or cloud costs. Read more here.

Modulos AG is a data debiasing service. In 2022, they released their data-centric platform that iteratively debiases data with a human-in-the-loop postprocess. Read more here.

Monitaur operationalizes AI governance. The company released a new product, "GovernML," allows one to maintain a system of record of model governance policies, ethical practices, and model risks. Find the press release here.

Category Trends

Each category in EAIDB represents a different "type" of ethical AI service and therefore display very different dynamics over the course of a year. Below are some highlighted trends that seem to be driving the industry forward.

For more information on each category, visit our methodology.

Open-Source Technology
Undermaintained and technologically lagging open-source packages provide individual developers with severely limited options.

It takes a village to support an open-source framework and keep it updated. Existing packages on the internet today lag behind the most current technology and are often not enough on their own to build truly responsible applications.

The following chart represents the average number of commits for several well-known open-source packages on Github. In blue are general AI/ML libraries; in red are responsible development toolkits. Notice the stark contrast in human capital.

But this is not something unexpected. These packages are necessary and fulfil a crucial role but cannot keep up with the human capital delivered to AI-enabling developer tools. These libraries are undermaintained because there are too few resources allocated to the problem of ethics in AI/ML. This may be changing, however, as some open-source technologies like BigScience Bloom and ChatGPT alternative PaLM + RLHF have reignited buzz around open-source developer tools. In the meantime, attention and resources continue to be delivered to AI enablers, not AI problem-solvers.

MLOps, Monitoring, and Observability
MLOps companies continue to garner attention from the investment community.

With companies like Arthur AI and Fiddler AI paving the way, this category of startups seems to be growing the fastest relative to the others. MLOps companies are reaching Series A investment rounds much faster on average, despite being earlier in its lifecycle relative to other categories like hiretech or data for AI companies.

The chart on the left depicts the proportion of each category / subcategory in EAIDB that have reached at least a Series A funding round. The chart on the right shows the general lifecycle of companies founded from 2015-2021 in each category. Note that the MLOps curve reached its peak much later than the others; this means the MLOps category has newer companies that have already exceeded growth expectations from a funding perspective.

This could be an indication that the investment world still views responsible AI as primarily a developer-centric problem. AI GRC companies, which typically come into play after models are built, seem to be struggling to prove (to investors specifically) that they are needed.

Data for AI
Data privacy and synthetic data markets forge forward towards maturity as acquisitions begin to materialize within the space.

In 2022, one of the earliest acquisitions within EAIDB's "Data for AI" category was made. Anonos, a data protection company specializing in PII obfuscation and privacy preservation, acquired Statice GmbH, a German synthetic data company.

Company Focus Status Last Funding Round
Statice GmbH finance, healthcare, insurance Acquired Seed
Clearbox AI context-agnostic Active Pre-Seed
Syndata AB context-agnostic Active Pre-Seed
Hazy context-agnostic Active Seed
Tonic context-agnostic Active Series B
Mostly AI finance, insurance Active Series B

Statice was unique in that they were really more of a targeted synthetic data company. In essence, they chose to focus their efforts in the financial vertical (and won some awards for it as well) and built functionality to cover every use case. They were specialists in a generalist space. Statice is one of the only early-stage synthetic data companies that do this. From Anonos' perspective, this makes Statice far more appealing than its competitors because Anonos' clients primarily operate in "financial services, media, and pharmaceuticals". In Anonos' words, "Statice's vision for data agility combined with privacy reliability changed how we drive digital transformation for our clients." Perhaps this is an indication that, in the responsible development sphere, it is better to dive vertically rather than spread horizontally if entering the market late.

Targeted Solutions and Technologies
New alternatives call existing methods of implementing machine learning into question.

Federated Learning

In cases where data privacy is of the utmost importance, methods of machine learning that access the data and bring it into a centralized platform (a warehouse, database, etc.) are typically considered risky, even with cutting-edge security. Federated learning, productionized by companies like Integrate AI allow algorithms to learn local methods over each dataset and report parameters back to a global model. Because of this scheme, the global model (the one put into production) has never seen a single row of the original data.

Causal AI

Spearheaded by firms like CausaLens, Causal AI is a branch of machine learning that takes causal effect as the primary learning mechanism instead of correlations. This makes causal AI inherently interpretable and can generate insights on a much more robust level. There are, of course, caveats with this approach, but the idea that all decisions are immediately auditable makes causal AI an attractive prospect.

Neurosymbolic AI

The newest of these three technologies (and arguably the most difficult to understand!), neurosymbolic AI is a combination of neural and logic-based symbolic architectures. The output of these models are neural-type networks that are inherently explainable, auditable, and interpretable. Companies like UMNAI are currently leading the charge.

These technologies, however, are still in their early stages. Though the AI climate is relatively warm to causal AI, it has not quite seen the full potential of federated learning or neurosymbolic AI. These paradigm shifts usually take years to really accelerate as switching costs away from traditional methods are excessively high for pre-existing applications. Gartner's Hype Cycle cites causal AI as likely to reach peak adoption in the next two to five years.

This category of startups includes any vertically-oriented startup (hiretech, fintech, etc.) as well as companies forming horizontally-oriented technologies (e.g. a company like AlgoFace which develops facial detection technology for any use case).

AI Governance, Risk, and Compliance
The market becomes increasingly saturated with new ethics specialist consulting firms. Existing AI GRC startups (non-consulting) experienced significant growth.

Consulting firms are generally considered lighthouses in the murky world that is AI ethics. With so many new frameworks, technologies, etc., many clients are understandably overwhelmed. They often turn to consulting firms to provide guidance. Some of the larger firms in the consulting space (the Deloittes, Accentures, McKinseys of the world) are often used as a first resort but often don't have the specific expertise necessary to solve the problem. The gap they have left in the market left a massive pain point for customers, which created an inflow of new firms over the last few years.

Note that EAIDB's discovery process lags new firm creation. The trend from 2018-2020 is expected to hold true in 2022.

Conclusion

Based on the patterns of innovation and momentum observed throughout 2022, EAIDB presents the following high-level insights and predictions.

  1. There are groundbreaking open-source movements coming.

    2. With such a large market gap in the open-source space, EAIDB expects major contributions from organizations well-poised to deliver impactful software directly to individuals. These organizations might be universities / research centers or governmental bodies. New open-source products, frameworks, and initiatives are expected to hit the market by 2H2023.

  2. Generalists will start adapting their technologies to follow a more targeted approach.

    3. Generalists (i.e., context-agnostic products) in this market typically lose to specialists (not a fixed rule, but holds empirically true). The name of the game in the ethical AI ecosystem is specificity, especially for newer companies. The depth of a product or service and its use case coverage within a vertical will play a critical role in how the market decides winners and losers. As a means of product differentiation, many companies will adapt their technology to offer better coverage for their first few clients and will therefore transform into a specialist firm. This is one theory as to why Statice GmbH was so coveted by Anonos (in addition to their outstanding proprietary technology).

  3. Hiretech as a subcategory will continue to slow and give way to other verticals.

    4. Hiretech (which boomed in the mid-2010s) is already slowing in terms of new companies founded because the subcategory has reached a stage of maturity. We are currently witnessing a lot of M&A activity in the particular subsector of "ethical hiretech." This may continue as the market consolidates a bit. Hiretech will soon be outpaced by other emerging sectors like text/vision, insurtech, healthtech, etc. Data privacy is expected to be the "next hiretech" as it only slightly lags hiretech in maturity. We have already seen M&A activity within the data privacy sector, and this is only expected to grow in momentum.

EAIDB Partnerships & Initiatives

In 2022, EAIDB partnered with two institutions to perpetuate knowledge and awareness of the ethical AI ecosystem and why it is so critical in today's automated world.

EAIDB and Nordic Innovation collaborated on a "Nordic Ethical AI Map" (viewable here) to increase awareness of organizations that are actively working to improve the way AI is built. Nordic Innovation is responsible for fostering cross-border trade and innovation in the Nordic region.

EAIDB and the Montreal AI Ethics Institute (MAIEI) collaborated on an ethical AI series covering each of the categories in the database. MAIEI regularly publishes content related to AI ethics.

About the Ethical AI Database (EAIDB)

The Ethical AI Database is a live database of curated startups attempting to solve some of the most damaging aspects of AI / ML in society. We offer semiannual market map updates and reports, but periodically release content through various media channels. For more on how we curate our database, view our methodology. To submit your company to our list, fill out the submission form. If you'd like to work with us, you can reach out here. Thank you to the Ethical AI Governance Group (EAIGG) for assisting us with our mission.

Disclaimer: logos were taken from LinkedIn company profiles or were found via search engines, but belong to their respective firms.

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