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Artificial Intelligence Disclosure Dangers: Lessons from the Telus


Artificial intelligence (AI) is reshaping the company panorama, providing transformative potential and fostering innovation throughout industries. However as AI turns into extra deeply built-in into enterprise operations, it introduces complicated challenges, notably round transparency and the disclosure of AI-related dangers. A latest lawsuit filed in the US District Court docket for the Southern District of New York—Sarria v. Telus International (Cda) Inc. et al., No. 1:25cv00889 (S.D.N.Y. Jan 30, 2025)—highlights the twin dangers related to AI-related disclosures: the risks posed by motion and inaction alike. The Telus lawsuit underscores not solely the significance of legally compliant company disclosures, but in addition the risks that may accompany company transparency. Sustaining a rigorously tailor-made insurance coverage program may also help to mitigate these risks.

Background

On January 30, 2025, a category motion was introduced in opposition to Telus Worldwide (CDA) Inc., a Canadian firm, together with its former and present company leaders. Recognized for its digital options enhancing buyer expertise, together with AI companies, cloud options and consumer interface design, Telus faces allegations of failing to reveal essential details about its AI initiatives.

The lawsuit claims that Telus failed to tell stakeholders that its AI choices required the cannibalization of higher-margin merchandise, that profitability declines may consequence from its AI growth and that the shift towards AI may exert better strain on firm margins than had been disclosed. When these dangers grew to become actuality, Telus’ inventory dropped precipitously and the lawsuit adopted. In response to the criticism, the omissions allegedly represent violations of Sections 10(b) and 20(a) of the Securities Change Act of 1934 and Rule 10b-5.

Implications for Company Danger Profiles

As we’ve defined beforehand, companies face AI-related disclosure dangers for affirmative misstatements. Telus highlights one other necessary a part of this dialog in the type of potential legal responsibility for the failure to make AI-related threat disclosures. Put otherwise, firms can face securities claims for each understating and overstating AI-related dangers (the latter usually being known as “AI washing”).

These dangers are rising. Certainly, in accordance Cornerstone’s recent securities class action report, the tempo of AI-related securities litigation has elevated, with 15 filings in 2024 after solely 7 such filings in 2023. Furthermore, each cohort of AI-related securities filings had been dismissed at a decrease fee than different core federal filings.

Insurance coverage as a Danger Administration Software

Contemplating the potential for AI-related disclosure lawsuits, companies might want to strategically contemplate insurance coverage as a threat mitigation device. Key issues embrace:

  1. Audit Enterprise-Particular AI Danger: As we’ve defined earlier than, AI dangers are inherently distinctive to every enterprise, closely influenced by how AI is built-in and the jurisdictions during which a enterprise operates. Firms might wish to conduct thorough audits to establish these dangers, particularly as they navigate an more and more complicated regulatory panorama formed by a patchwork of state and federal insurance policies.
  2. Contain Related Stakeholders: Efficient threat assessments ought to contain related stakeholders, together with numerous enterprise items, third-party distributors and AI suppliers. This complete strategy ensures that each one sides of an organization’s AI threat profile are totally evaluated and addressed
  3. Contemplate AI Coaching and Academic Initiatives: Given the quickly growing nature of AI and its corresponding dangers, companies might want to contemplate training and coaching initiatives for workers, officers and board members alike. In spite of everything, growing efficient methods for mitigating AI dangers can flip in the first occasion on a familiarity with AI applied sciences themselves and the dangers they pose.
  4. Consider Insurance coverage Wants Holistically: Following business-specific AI audits, firms might want to meticulously evaluate their insurance coverage applications to establish potential protection gaps that might result in uninsured liabilities. Administrators and officers (D&O) applications may be notably necessary, as they’ll function a crucial line of protection in opposition to lawsuits just like the Telus class motion. As we defined in a recent blog post, there are a number of key options of a profitable D&O insurance coverage evaluate that may assist improve the chance that insurance coverage picks up the tab for potential settlements or judgments.
  5. Contemplate AI-Particular Coverage Language: As insurers adapt to the evolving AI panorama, firms must be vigilant about reviewing their insurance policies for AI exclusions and limitations. In instances the place conventional insurance coverage merchandise fall quick, companies would possibly contemplate AI-specific insurance policies or endorsements, reminiscent of Munich Re’s aiSure, to facilitate complete protection that aligns with their particular threat profiles.

Conclusion

The mixing of AI into enterprise operations presents each a promising alternative and a multifaceted problem. Firms might want to navigate these complexities with care, making certain transparency of their AI-related disclosures whereas leveraging insurance coverage and stakeholder involvement to safeguard in opposition to potential liabilities.



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