While many types of AI have existed in some form for several
years, the new generation of sophisticated AI solutions, especially
generative AI (“GenAI”), has driven a rise in AI use
cases, including tools to create and debug software code, mint
collections of blockchain-based NFTs and produce other content,
automate responses to customer queries via chatbots (and create
help center scripts), summarize customer reviews and datasets,
screen resumes and analyze video interviews, and produce real-time
translations, among others. This session, “Key Issues to
Consider When Investing In or Contracting With AI Businesses,”
addresses legal and practical concerns for businesses that are
seeking to procure AI systems or invest in AI business, or, on the
other hand, AI businesses seeking to provide their software to the
market and prepare for further investment. The key question for
organizations can be boiled down to: Is there anything to keep in
mind when contracting for AI systems or investing in AI ventures?
The answer is “Yes,” with the presenters outlining
important, practical issues during the webinar, including: (1) the
key risk issues when contracting for GenAI and how such issues can
be managed; and (2) investment due diligence and transactional
agreement issues when considering M&A and AI-related business
targets.
The terms governing use of an AI system and the legal issues
raised can vary depending on the service’s particular
attributes, which might range from:
- Free vs. paid
- Shared infrastructure vs. dedicated infrastructure
- Provider’s own user interface vs. API access
- Sophistication of architecture
- Ability of user to train using their own datasets or ability to
customize - Cloud hosted (SaaS) vs. locally deployed (licensed)
- Open source vs. proprietary
- Size and diversity of training dataset; quality of outputs and
languages recognized
Contracting with AI Businesses
The presenters discussed how companies should manage the risks
that come with procuring and using a GenAI system and what risks AI
developers should expect to be raised by their customers.
The presenters pointed to five general risks of using GenAI
under agreements with AI providers and mechanics to mitigate and
manage such risks (noting that such management will include
contractual protections, but that such provisions “only go so
far” and negotiations must be targeted in approach). Thus, the
presenters stated that practical, operational mechanics are also
needed (a/k/a “guardrails”) and that risks and
protections will change depending on the GenAI tool and how it is
being used.
- Risk 1: Accuracy of output and bias. As
previously discussed in prior webinars in this series, no business
should assume that GenAI output is 100% accurate, as systems are
not designed to always produce the “right answer” and may
fabricate facts and sources (and GenAI training data may be
inaccurate, incomplete and not jurisdiction-specific and may
produce discriminatory output). As the presenters noted, GenAI
product terms will sometimes disclaim all responsibility for the
quality of outputs. Depending on one’s bargaining position, a
user might negotiate for acceptable data standards and controls
focusing on accuracy of output products and removal of bias.
However, as noted by the presenters, such a position may be a
“non-starter” for many GenAI providers as the most
popular GenAI tools right now are not bespoke products and likely
cannot be technically tailored (outside of certain GenAI tools that
allow users to fine-tune using their own datasets). Thus, the
presenters stated that if a contractual protection of this sort is
not available, users will need to resort to practical guardrails to
mitigate inaccuracy and bias, including: designing queries with
focus on jurisdictions and supplementing outputs with information
from other reliable sources; conducting human review for more risky
outputs; and considering insurance policies to cover such
risks. - Risk 2: Confidentiality. Generally speaking,
inputs and outputs from publicly-available GenAI tools may be used
by the provider for training purposes. Thus, there is the potential
for breaches of confidentiality, IP leakage, loss of legal
privilege and loss of trade secret protection when such information
is inputted into a public-facing GenAI interface. Moreover, the
presenters also stated that inputting third party licensed data or
software into a public GenAI tool might breach contractual
obligations. To mitigate this risk, the presenters stated that
users should engage AI providers as to terms of confidentiality and
non-sharing restrictions, particularly for protecting prompts and
other inputs. Some providers, such as OpenAI, offer such
protections to its paid, enterprise users; on the other hand,
ChatGPT’s public version offers no such standard protections
and it is up to the user to take practical steps to mitigate this
risk, such as affirmatively opting-out of any data sharing through
privacy setting controls; limiting submission of confidential
information or rewording or disaggregating important elements of
confidential information (e.g., submitting snippets of code without
context). - Risk 3: Security. The presenters stated that
businesses need to be cautious when integrating GenAI solutions
into their systems, as this rapidly developing technology may have
unknown vulnerabilities. GenAI products may either be hosted and
managed by a service provider or downloaded and deployed on the
customer’s own infrastructure. From a contractual standpoint,
the presenters stated that users can seek contractual protections
and security assurances from AI providers, though, again, this may
be difficult to negotiate outside of a customized GenAI arrangement
and users deploying both enterprise and standardized products
should examine the terms related to data security (with the former
likely offering additional security assurances than the latter) or
look to existing products that offer GenAI tools that already
promise certain levels of security. Practically speaking, the
presenters stated that GenAI tools should be pre-checked before
downloading, as with any software product, and that protections
should be put in place to monitor data flow against
“poisoning” of training data or related AI-related
“inference” attacks that might allow an attacker to infer
what training data was used to train the model. - Risk 4: Privacy. The presenters stated that
submitting personal data into an AI model could infringe privacy
rights of data subjects; the user of personal data in outputs could
be similarly infringing of the rights of individuals. Thus, from a
contractual standpoint, the presenters stated that a data
processing agreement should be entered into by the user and the AI
provider that processes personal data on the user’s behalf,
with such agreement covering various aspects of privacy and
security. While the presenters pointed out that a data processing
agreement might be fairly standard in nature, users might
scrutinize provisions related to privacy liability and how GenAI
providers characterize their role under data privacy regulations
(i.e., whether they are data processors or data controllers). Other
practical considerations when protecting against privacy risk
include: using available settings to opt out of any data sharing;
avoiding sharing any personal data unless truly required; and
reviewing privacy policies. - Risk 5 – Intellectual Property. The
presenters pointed to an earlier webinar in this series that
covered the IP issues, but wanted to briefly cover some general IP
concerns surrounding the use of GenAI, such as the fact that the
training of the model could potentially be considered an infringing
use of copyrighted works and certain output could be deemed to be
an unauthorized derivative work of third party content (not to
mention that registration of GenAI output is on unclear legal
footing). As for contractual protections, the presenters suggested
that users examine terms concerning the statement of ownership of
inputs and outputs and indemnities as to infringement, as different
GenAI tools handle these issues differently.
Overall, the presenters stressed that, on the contractual side,
it helps to have counsel with experience in this area that
negotiate specific risk items rather than academic points. To
further mitigate risk, the presenters suggested companies produce
GenAI-focused internal documents, policies and training materials
that outline Do’s and Don’ts and explain risks for specific
GenAI tools.
Investment in AI Companies
In the second part of this webinar, the presenters went over
some issues to consider when investing in an AI business (which
might be described as one that hosts and offers an AI service or
uses AI as an integral part of its business functions). Investors
might first seek to uncover the core value in an AI service
provider as well as the primary revenue drivers for a provider,
whether it concerns rights to datasets (including third party
licenses), proprietary data collected by the company, proprietary
models used to ingest and analyze the data, key personnel and
engineers, or rights to use user inputs and outputs for training
purposes. AI-related deals raise some similar issues to other
technology deals related to software and SaaS, but the presenters
stressed that there are a host of due diligence issues unique to AI
that are relevant to investment in or acquisition of an AI company
and knowledge of these issues can help buyers structure deals and
tailor representations and warranties in purchase agreements and
sellers manage operations to attract further investment.
The presenters noted that the terms of use or license agreement
are foundational documents, and investors should understand what
positions the target company is taking on key issues of potential
liability, data sharing, allowance for using user prompts for
training purposes, or potential over-commitments for accuracy or
non-infringement. Investors might also ask additional questions,
including:
- What level of negotiability does the provider have with
customers? - What exposure does the company have to risk areas previously
discussed in this webinar? - How is the AI trained and how are models and outputs
validated? - What are the company’s practices with compliance with IP
and other laws and relevant contracts with third parties? - Does the provider use only lawfully-procured data according to
data procurement standards and controls? - Does the AI company have any gaps with respect to warranties
and indemnities with their data providers? - Does the AI company have any contracts with third party data
providers that limit its flexibility or compel it to give up rights
in the underlying technology? - Are there any issues related to UK IP issues related to text
and data mining? - As to cybersecurity, what protocols has the AI company put in
place, and how is the data stored, and where? - Some additional questions might include operational queries,
including: How will the target’s AI business be integrated into
the buyer’s existing business? Are there any implications from
open source elements of the target’s platform?
The presenters also brought up some US-related due diligence
issues with respect to data scraping and data mining, and the
variety of potential legal claims that a website operator could
make against a data scraper, such as liability under the Computer
Fraud and Abuse Act (CFAA), which prohibits access to a protected
computer “without authorizations” or that “exceeds
authorized access,” most pertinently related to website pages
that are not publicly accessible. Additionally, the webinar
discussed additional U.S. legal issues investors should consider,
including copyright infringement liability (subject to a fair use
defense vs. the UK’s narrower “fair dealing”
defense), and breach of contract for violating website terms of
service, which may or may not be enforceable in all cases and
instances. Overall, the presenters stressed that investors should
assess potential infringement risks in training data and outputs:
What rights does the AI provider have to user prompts and outputs?
If AI company users its own AI to develop outputs, is ownership of
output clear? What IP protection is available for the company’s
algorithms and models?
As to due diligence related to compliance with laws and
regulations, the presenters noted that investors should consider a
host of relevant laws, including U.S. and EU privacy laws,
biometric privacy laws, industry-specific laws (e.g., healthcare),
and employment laws (particularly with respect to employment
discrimination, including local laws such as a recently-enacted New
York City AI-related employment regulation). Rules on foreign
investment and national security export regulations may also be
relevant, depending on the nature of the AI system.
Looking ahead, it is important for an investor to also look
long-term, and closely examine how the AI provider is positioning
itself with respect to future regulations and other emerging AI
principles that have captured regulators’ attentions. These
might include: potential for outputs to influence undesirable human
behavior, discrimination through bias, exploitation of vulnerable
groups, and real-time biometric profiling, as well as use of AI
processing of personal information to use in automated decision
technology. As for the EU, for example, the AI Regulation is
forthcoming and will place the most regulation upon high-risk AI
tools. It is also important to note that the AI Regulation will
have extraterritorial effect, thus impacting UK and U.S. entities
with global operations. The AI Regulation is expected to place
additional regulatory burdens on AI companies that may increase
exposure and cost for investors in such companies (particularly
those that handle so-called high-risk tools). Investors should
examine how AI companies are incorporating policies and procedures
to comply with existing and upcoming AI regulations –
otherwise, if an AI company must later re-engineer their
technology, this will have an adverse impact on an investment.
The presenters posited additional due diligence questions that
might be asked when a buyer is acquiring a company that uses GenAI,
a fact that may not be obvious without performing some
investigation. Questions include: How does the target use GenAI?
What is the nature of information the company includes in this
their prompts? What are the governing terms of service with the
GenAI provider? How is the GenAI hosted? Is the company using its
own data to train the GenAI system? Are there any ambiguities in
ownership in AI outputs?
In all, this is an exciting time for users, developers,
investors, but also presents legal risks that need to be carefully
managed. While the diligence approach is similar in some respects
to other tech deals, the presenters closed with a reminder to
viewers that AI-related issues need to be considered through a
special lens and ideally by a practitioner or team with experience
in this emerging area.
Key Issues To Consider When Investing In Or
Contracting With AI Businesses
The content of this article is intended to provide a general
guide to the subject matter. Specialist advice should be sought
about your specific circumstances.
