Securities Class Actions and Enhancing Transparency and Accountability [2026]

Introduction to Securities Class Actions and Enhancing Transparency and Accountability

• Securities class actions are rapidly evolving due to technological advances, especially artificial intelligence (AI).
• AI-related securities class actions have surged:
  • More than doubled in 2024 compared to 2023.
  • Nine AI-related cases were filed in just the first half of 2025.
• This trend highlights the urgent need for greater transparency in securities litigation, particularly for companies leveraging AI in their operations and marketing.
• The stakes are high for both investors and companies:
  • AI-related securities cases survive motions to dismiss 30%-50% more often than traditional securities claims.
  • This leads to increased financial and reputational risks.
• Total settlement dollars in securities litigation reached $3.8 billion in 2024.
  • The top 10 settlements accounted for about 60% of this total.
• The landscape of securities litigation has fundamentally changed due to:
  • Digital assets
  • ESG disclosures
  • Artificial intelligence
• Case resolutions increased by 17% in 2024, with 217 cases resolved—breaking a six-year downward trend.
• Careful analysis shows that clear and open AI disclosures can help reduce legal risks.
• This piece will:
  • Examine solutions for enhancing transparency in securities litigation.
  • Highlight best practices and relevant regulations shaping the field in the evolving AI landscape of 2025.

If you suffered substantial losses and wish to serve as lead plaintiff in a securities class action, or have questions about AL disclosures, AL washing, AL claims, or securities litigation, or just general questions about your rights as a shareholder in securities class actions, please contact attorney Timothy L. Miles of the Law Offices of Timothy L. Miles, at no cost, by calling 855/846-6529 or via e-mail at [email protected].(24/7/365).

The Rise of Challenges in Enhancing Transparency in Securities Litigation

• Companies rushing to leverage AI trends are driving new transparency challenges in securities litigation to all-time highs.
• The fast-paced digital environment creates fertile ground for deceptive practices, leaving investors vulnerable and companies exposed to increased regulatory scrutiny.

AI-Washing and Exaggerated Claims in Public Filings

• AI-washing refers to companies making false or exaggerated statements about their AI capabilities in public disclosures.
  • Regulatory reports reveal this tactic is on the rise as businesses seek investor attention by spotlighting supposed tech advancements.
  • The SEC has flagged these issues, intensifying its scrutiny of firms making unsubstantiated AI claims.
• Common patterns of AI-washing include:
  • Falsely claiming integration of AI in decision-making processes
  • Exaggerating the sophistication of current AI systems
  • Presenting manual processes as “AI-powered” automation
  • Claiming proprietary AI development while actually using third-party solutions
• SEC enforcement actions:
  • In March 2024, the SEC settled charges with two investment advisers for misleading investors about their use of AI.
    • Delphia made false ads about leveraging client data and AI for more accurate investment decisions—despite lacking such capabilities.
    • Global Predictions falsely touted “expert AI-driven forecasts” and branded itself as the “first regulated AI financial advisor.”

The Specter of an “AI Bubble”

• Public enthusiasm for AI has sparked a flood of disclosures from companies eager to attract capital—reminiscent of the late-1990s dot-com bubble.
• Many firms achieve sky-high valuations despite limited revenue, raising concerns about a potential “AI bubble.”
• If the bubble bursts, it could unleash a surge of securities fraud class actions similar to those seen after the dot-com collapse.

Investor Losses from Misleading AI Disclosures

• AI-washing has caused significant financial harm:
  • Stock prices often plunge when the truth about inflated AI claims emerges.
  • Example: Innodata’s stock dropped ~30% after allegations that it misrepresented manual labor as “AI-powered” data preparation.

• AI-related securities class actions have surged in 2025:
  • • 15 cases have already been filed so far this year—more than double the seven cases filed in all of 2024.
    • These lawsuits typically follow sharp stock price drops triggered by revelations of AI misrepresentation.
    • Sector breakdown (2025 YTD):
      • Technology: 8 cases
      • Communications: 4 cases
      • Industrial: 2 cases
      • Consumer: 1 case
• Why these cases are more likely to survive dismissal:
  • Courts resolve AI-related securities suits 30%-50% less often on motions to dismiss versus traditional securities class actions.
    • Companies face longer litigation, higher settlements, and greater reputational damage.

If you suffered substantial losses and wish to serve as lead plaintiff in a securities class action, or have questions about AL disclosures, AL washing, AL claims, or securities litigation, or just general questions about your rights as a shareholder in securities class actions, please contact attorney Timothy L. Miles of the Law Offices of Timothy L. Miles, at no cost, by calling 855/846-6529 or via e-mail at [email protected].(24/7/365).

Legal Standards and Court Rulings

• Puffery vs. Actionable Claims:
  • Vague, optimistic statements about AI (“fairly magical thing”) are considered non-actionable puffery.
  • Specific, verifiable claims of “significant advantage” can be actionable if proven false.
• SEC Enforcement Message:
  • The SEC warns that all AI claims must be accurate and substantiated by evidence.
  • Former SEC Chair Gary Gensler: “If you claim to use AI in your investment processes, you need to ensure that your representations are not false or misleading.”

Why AI Cases Face Stricter Judicial Scrutiny

• Judges are applying heightened scrutiny to AI disclosures, demanding transparency and accuracy.
• The complexity and novelty of AI technology make courts reluctant to dismiss without deeper factual investigation.
• Growing transparency requirements mean any misleading disclosure receives extra attention from courts.
• Many cases are event-driven—triggered by data security incidents, regulatory probes, or missed financial projections.

Implications for Companies

• More Cases Progress:
  A larger proportion of AI-related securities lawsuits advance past the initial dismissal phase, resulting in increased settlements and ongoing litigation.
• Increased Legal Risk:
  Firms developing or deploying AI now face a higher risk of prolonged legal challenges tied to their public statements.
• Focus on Disclosure:
  The trends underscore the urgent need for companies to be transparent and precise when describing their AI capabilities and risks in public filings.

Key Drivers Behind Transparency Failures in AI-Related Securities Litigation

• Lack of Internal Controls Over AI-Related Statements
  • Many companies are rapidly adopting AI technologies without establishing robust internal control systems.
  • Weak controls make it difficult to ensure the accuracy and reliability of public statements about AI capabilities.
  • Internal audits are crucial for verifying AI claims before sharing information with investors. However, many organizations skip this step, increasing litigation risk.
  • External auditors provide an added layer of credibility by independently reviewing AI-related practices, boosting trust among regulators and investors.
• Lack of Internal Controls Over AI-Related Statements
  • The SEC now intends to hold executives personally responsible for misleading or inadequate AI disclosures—mirroring its approach to cybersecurity lapses.
  • Regulators will investigate whether leadership was aware of false or exaggerated statements about AI.
• Essential Elements of Strong AI Governance
  • Comprehensive AI-specific policies and procedures
  • Clearly defined oversight responsibilities across departments
  • Transparent reporting structures
  • Specialized internal controls tailored to the company’s specific AI use cases
• Recommended Controls for Effective Oversight
  • Data validation checks
  • Algorithm testing and performance reviews
  • Human supervision of automated processes
• Current Gaps and Risks
  • Most companies still lack a complete system of internal controls for their AI activities.
  • This gap is increasingly problematic as accounting and auditing errors rise, highlighting the urgent need for better governance practices.

Inconsistent Definitions of AI Across Corporate Materials

• Varied AI terminology creates major transparency challenges:
  • Companies often use different terms and definitions for AI across their public documents and disclosures.
  • This inconsistency confuses investors and regulators, making it difficult to assess true AI capabilities.
• Regulatory spotlight on clarity:
  • The SEC flagged this as a significant issue in 2025.
    • About 61% of companies were asked by the SEC to explain how they use AI and to disclose related risks.
    • 17% faced direct SEC questions about unclear or inconsistent AI terms in their filings.
• Lack of a standard definition:
  • No single, universally accepted definition of “AI” exists—regulators in different regions have their own interpretations.
  • These regulatory gaps make it hard for companies to know exactly what is required in their disclosures.
  • Discrepancies often arise between marketing claims and official SEC filings due to these definitional differences.
• SEC guidance and expectations:
  • The SEC describes AI as “the most revolutionary technology today,” but warns against exaggerating capabilities (“AI-washing”).
  • Without a clear regulatory definition, companies must still justify their AI claims with solid evidence and consistent messaging.
  • Consistency is expected across all communication channels:
    • SEC filings
    • Investor presentations
    • Earnings calls
    • Marketing materials
    • Company websites
    • Press releases
    • Social media
• Cross-functional collaboration required:
  • Management, communications, marketing, investor relations, and technology teams must coordinate closely.
  • Legal teams with technical knowledge should review all AI-related statements to ensure they reflect actual capabilities—not just aspirations.

Companies Should Focus on a Multi-Faceted Approach to AI Transparency

To enhance transparency and reduce legal risk, companies should address every stage of the AI lifecycle—from data collection to deployment—using governance, technical solutions, and clear communication.

Governance and Process Steps

• Establish a responsible AI framework:
  • Implement formal governance structures with clear ethical principles and policies.
  • Assign defined roles and responsibilities for AI outcomes.
  • Align frameworks with emerging regulations (EU AI Act, NIST AI Risk Management Framework).
• Document everything:
  • Maintain thorough records across the entire AI lifecycle (data sources, preprocessing, model architecture, evaluation metrics).
  • Ensure traceability and accountability through strong data provenance.
• Conduct regular audits:
  • Schedule both internal and external audits of AI systems.
  • Monitor performance, detect/mitigate bias, and ensure compliance with ethical/legal standards.
• Engage stakeholders:
  • Involve diverse groups—including non-technical staff and end-users—in design, development, and deployment.
  • Early engagement helps identify potential biases or misinterpretations.
• Implement a feedback loop:
  • Provide users with clear channels to report or contest incorrect AI outputs.
  • Use feedback to retrain models and better align with user expectations.

Technical and Model-Related Steps

• Use explainable AI (XAI) techniques:
  • Apply methods that generate understandable explanations for decisions (not “black box”).
    • Highlight key input variables (“feature importance”).
    • Use local methods (LIME, SHAP) for single predictions; global methods for overall model logic.
    • Visualize decision-making processes for interpretable models like decision trees.
• Prioritize data transparency:
  • Disclose how training data was sourced and collected.
  • Explain what types of data were included/excluded—and why.
  • Clearly communicate efforts to identify and mitigate bias in training data.
• Label AI-generated content:
  • Clearly indicate when content or decisions are produced by an AI system (e.g., chatbots identifying as AI assistants).
  • Sets realistic expectations and avoids misleading users.

Communication and User-Focused Steps

• Communicate capabilities and limitations:
  • Be upfront about what the AI system can—and cannot—do.
  • Provide context so users understand appropriate use cases.
• Provide tiered explanations:
  • Offer simple, jargon-free explanations for end-users.
  • Give regulators/technical experts access to detailed reports (performance metrics, bias analyses).
• Disclose data usage plainly:
  • Draft easy-to-understand privacy policies covering how customer data is handled by the AI system.
  • Obtain explicit consent for any data use; build trust via clarity.
• Publish transparency reports:
  • Release periodic public reports on system performance, risks identified, mitigation steps taken, and real-world impact.
  • Demonstrates proactive commitment to accountability.

Regulatory Focus on AI Disclosures and Transparency

• Federal regulators have intensified their scrutiny of AI-related corporate disclosures:
  • The SEC and DOJ are actively pursuing misleading statements about AI in public markets.
  • This oversight is crucial for improving transparency in securities litigation and protecting investors from “AI-washing.”

SEC Enforcement under Rule 10b-5

• Rule 10b-5 (Exchange Act Section 10(b)) is the primary anti-fraud mechanism:
  • Bans material misrepresentations and misleading omissions in securities transactions.
  • Violations require proof of scienter (intent to deceive, manipulate, or defraud); reckless conduct can satisfy this standard.
• Recent enforcement actions (2024–2025):
  • The SEC settled with a consumer technology company in January 2025 for falsely claiming its AI eliminated human intervention from order processing—when most orders still required manual handling.
  • Investment advisory firms have also been charged for overstating AI’s role in investment decisions.
    • Materiality is key: misstatements are actionable if they would likely influence a reasonable investor’s decisions.
• Individual Liability:
  • The SEC examines whether executives knew or should have known about misrepresentations, similar to cybersecurity disclosure cases.
  • Leaders who act in good faith and take reasonable steps to ensure accurate disclosures face lower risk of personal liability.

DOJ Involvement in Deceptive AI Marketing Cases

• The DOJ has begun criminal prosecutions for serious cases of AI-related fraud:
  • On April 9, 2025, the DOJ and SEC filed parallel actions against Albert Saniger (former CEO of Nate, Inc.).
    • Saniger allegedly raised over $42 million by falsely claiming Nate’s app used AI to complete orders automatically—with success rates above 90%.
    • In reality, human workers in the Philippines and Romania processed transactions manually.
• Criminal indictment details:
  • Charges include securities fraud and wire fraud (each carrying up to 20 years’ imprisonment).
  • Acting U.S. Attorney Matthew Podolsky emphasized that such deception harms investors, diverts capital from real innovation, and impedes true AI progress.

Key Takeaways

• Parallel SEC/DOJ enforcement sends a clear signal:
  • Both civil (SEC) and criminal (DOJ) penalties may apply for deceptive AI disclosures.
    • SEC seeks injunctive relief, civil penalties, disgorgement of profits.
    • DOJ pursues imprisonment, forfeiture, restitution.
• Regulators are setting boundaries:
  • Existing anti-fraud laws fully apply to emerging technologies like AI.
  • Companies must ensure all public statements about AI are accurate, substantiated, and not misleading—regardless of technological novelty or complexity.

If you suffered substantial losses and wish to serve as lead plaintiff in a securities class action, or have questions about AL disclosures, AL washing, AL claims, or securities litigation, or just general questions about your rights as a shareholder in securities class actions, please contact attorney Timothy L. Miles of the Law Offices of Timothy L. Miles, at no cost, by calling 855/846-6529 or via e-mail at [email protected].(24/7/365).

Corporate Governance Structures to Ensure Transparency in AI Disclosures

Importance of Good Governance

• Strong governance is essential for transparency in AI-related disclosures and for minimizing securities litigation risk.
• Companies using advanced technologies must build resilient oversight mechanisms.

AI Ethics Committees with Cross-Functional Oversight

• Cross-functional AI ethics committees are at the core of effective governance frameworks.
  • Membership typically includes:
    • Legal and compliance teams (regulatory risk evaluation)
    • Data scientists and AI experts (technical insight)
    • HR professionals (workforce impact)
    • Product and engineering teams (aligning development with governance)
    • Executive sponsors (strategic vision and support)
• These committees ensure AI aligns with ethical standards, regulatory needs, and company values.
• Without incentives and cross-team participation, there’s a risk of “governance washing” (talking about responsible AI without real action).
• Example: The Mayo Clinic’s dedicated AI governance structure prioritizes patient safety and ethics—helping maintain trust while scaling medical AI.
• Companies with formal AI governance teams are 44% more likely to successfully scale AI initiatives with proper controls.

Board-Level Review of AI Risk Disclosures

• Board involvement in AI oversight is rapidly increasing.
  • S&P 500 companies mentioning board oversight or AI expertise in proxy statements jumped 84% from 2023 to 2024, and over 150% since 2022.
  • As of mid-2025, about 31.6% of S&P 500 companies report board-level oversight of AI (via committees, director expertise, or ethics boards).
• Three main board oversight structures:
  1. Whole-board oversight: Suitable for smaller companies; keeps AI on the regular agenda.
  2. Expanded scope of existing committee: Often Audit (controls/disclosures) or Risk (systemic hazards/resilience).
  3. Dedicated Technology or AI committee: Common in data-driven sectors where AI is strategic.
• The trend has shifted toward full-board oversight by late 2024, reflecting the broader risk profile of AI beyond cybersecurity.
• Boards should collaborate closely with technical, legal, and risk-management leaders for holistic evaluation.

Disclosure & Accountability

• Only about 11% of S&P 500 companies openly disclose full board or committee oversight of AI.
• Utilities sector leads among S&P 500 firms reporting board-level oversight—19% in mid-2025, up from just 3% a few years prior.
• Best practice: Charters should define clear responsibilities for AI, set information/reporting requirements, meeting frequency, and require minutes that reflect robust director engagement.
• Management should designate an executive owner for internal governance—often the CIO, CDO, or Chief AI Officer—to ensure accountability for strategy and risk.

If you suffered substantial losses and wish to serve as lead plaintiff in a securities class action, or have questions about AL disclosures, AL washing, AL claims, or securities litigation, or just general questions about your rights as a shareholder in securities class actions, please contact attorney Timothy L. Miles of the Law Offices of Timothy L. Miles, at no cost, by calling 855/846-6529 or via e-mail at [email protected].(24/7/365).

Best Practices for Transparent AI Disclosures

Establishing transparent, accurate AI disclosures is essential to reduce securities litigation risks and build investor trust.

1. Define AI Capabilities Consistently Across Filings

• Use precise, consistent definitions:
  • Clearly explain what “artificial intelligence” means in the context of your business.
  • Specify whether you use machine learning, predictive models, generative AI, or other forms.
• Respond to SEC expectations:
  • The SEC has focused on unclear definitions—about 17% of its comments address problematic AI terminology.
  • Define terms like “generative AI” and “machine learning” with reference to your actual operations.
• Distinguish present from future capabilities:
  • Clarify what your AI systems currently do versus what you aim for them to do in the future.
• Differentiate internal vs. external solutions:
  • Make clear which technologies are developed in-house and which are provided by third parties.
• Maintain consistency:
  • Use the same definitions across all filings, press releases, and investor presentations.
• Avoid “AI-washing”:
  • Do not exaggerate or mislead about your company’s true AI capabilities—a growing concern as over 72% of Fortune 500 companies adopted AI technologies in 2024.

2. Disclose Data Sources, Limitations, and Risks

• Be transparent about data usage:
  • Specify whether your AI systems use public datasets, third-party data, or internal information.
  • Detail the types and volumes of data used for training algorithms.
• Explain data sharing and security practices:
  • Describe how you share data with partners or vendors and outline privacy safeguards.
• Address algorithmic risks:
  • Disclose potential risks of bias or discrimination inherent in your models.
  • Explain compliance with relevant regulations (e.g., EU AI Act).
• Clarify vendor dependencies:
  • State if your operations rely on external providers—34% of recent SEC comments require such explanations.
  • Discuss contingency plans if relationships with key providers change or end.

3. Regular Model Validation and Limitations Disclosure

• Describe validation processes:
  • Explain how often models are tested/validated and cite their commercial track record if possible.
• Be open about limitations:
  • Clearly communicate any known weaknesses or boundaries of your AI solutions.

4. Link Disclosures to Governance Practices

• Showcase oversight and controls:
  • Detail board-level oversight, risk management processes, and ethical guidelines related to AI deployment.
• Demonstrate proactive transparency:
  • Address these elements up front to shape your company’s narrative before regulators or litigants intervene.

Summary: By defining terms clearly, disclosing key risks and dependencies, validating models regularly, and linking disclosures to governance practices, companies can meet regulatory expectations and build long-term investor confidence.

Investor Due Diligence in the Age of AI-Driven Litigation

AI-related securities litigation is increasing rapidly. Investors must modernize their due diligence to address the new risks posed by companies making AI claims.

Evaluating AI Claims in Earnings Calls and Filings

• Scrutinize all AI-related statements:
  Investors should review how companies define and discuss “artificial intelligence” across earnings calls, marketing materials, and regulatory filings.
• Seek consistent, precise definitions:
  Companies should clarify whether they use machine learning, predictive models, generative AI, or other forms—and use these terms consistently.
• Watch for balanced risk disclosures:
  In 2024, negative mentions of AI risks increased, signaling a shift toward more realistic reporting. One-sided positive narratives may indicate “AI-washing.”
• Distinguish substance from hype:
  Academic studies show that detailed disclosures with clear implementation plans support higher valuations, while vague or irrelevant AI mentions do not.

Assessing Governance Frameworks for Risk Mitigation

• Evaluate AI governance structures:
  Investors should look for evidence of cross-functional AI ethics committees—including legal, technical, and business representation.
• Board oversight matters:
  As of 2025, about 31.6% of S&P 500 companies report direct board involvement in overseeing AI.
• Reference industry standards:
  Use frameworks like the NIST AI Risk Management Framework or “red/yellow/green light” risk classification tools to assess governance quality.
  • Red-light: Prohibited uses (e.g., mass surveillance)
  • Yellow-light: High-risk projects needing close oversight
  • Green-light: Low-risk applications
• Companies with robust governance generally experience better long-term outcomes and fewer lawsuits.

Lessons from High-Profile Securities Litigation Cases

Recent cases offer practical insights into evolving litigation risks:

• CrowdStrike’s July 2024 Software Failure
  • Faulty update crashed 8.5 million Windows computers globally.
  • $25 billion market value loss; stock dropped 32% in 12 days.
  • Shareholder class action claimed misleading disclosure about software testing; Delta Air Lines reported $500 million in losses and considered legal action.
  • Key lesson: Overstated assurances about validation/testing can trigger significant liability after operational failures.
• GitLab’s Missed Projections Tied to AI Overpromises
  • Allegedly oversold its AI features while raising prices; subsequent disappointing revenue growth led to a stock drop of 21%.
  • Securities suit dismissed—court found most statements were “forward-looking or corporate puffery,” not actionable misrepresentation.
  • Key lesson: Not all optimistic statements are legally actionable; specificity and context matter.

Conclusion

AI-related securities litigation has surged between 2024 and 2025, serving as a clear warning for companies leveraging artificial intelligence. Securities class actions have doubled, with most targeting organizations that made false or exaggerated claims about their AI capabilities—a trend reminiscent of the dot-com bubble’s overpromising.

The primary drivers of legal risk are clear:

• Weak internal controls over AI-related statements
• Inconsistent definitions of AI across corporate documents

Without rigorous verification systems and standardized terminology, companies leave themselves exposed to regulatory scrutiny and costly lawsuits. Regulators—including the SEC and DOJ—have ramped up both civil and criminal enforcement against misleading AI disclosures.

Recent high-profile cases (e.g., Super Micro Computer, Innodata, Oddity Tech) illustrate the devastating financial impact when inflated AI claims unravel. Stock prices can plummet, and investors often bear the brunt of these losses.

Best Practices for Companies:

• Establish robust governance structures—such as cross-functional AI ethics committees
• Ensure board-level oversight of AI risk disclosures
• Use consistent, precise definitions for all AI-related terms in public filings
• Fully disclose data sources, system limitations, and potential risks

Best Practices for Investors:

• Conduct thorough due diligence by analyzing AI claims in filings and earnings calls
• Assess corporate governance frameworks to identify potential litigation risks

The lesson from recent litigation is simple: Companies must balance technological ambition with honest, transparent communication. Those that invest in strong governance, consistent disclosures, and genuine transparency will build trust—and thrive—in today’s complex digital marketplace. In contrast, those engaging in “AI-washing” face major legal and financial consequences.

Transparency is now the bedrock of responsible AI adoption in the modern securities market.

If you suffered substantial losses and wish to serve as lead plaintiff in a securities class action, or have questions about AL disclosures, AL washing, AL claims, or securities litigation, or just general questions about your rights as a shareholder in securities class actions, please contact attorney Timothy L. Miles of the Law Offices of Timothy L. Miles, at no cost, by calling 855/846-6529 or via e-mail at [email protected].(24/7/365).

Key Takeaways

AI-related securities litigation has surged dramatically, with companies facing severe consequences for misleading investors about their artificial intelligence capabilities and implementation.

• AI-related securities class actions more than doubled in 2024, with cases 30-50% more likely to survive dismissal motions than traditional securities claims.

• “AI-washing” – exaggerating or falsely claiming AI capabilities – triggers SEC enforcement under Rule 10b-5 and DOJ criminal charges in severe cases.

• Companies must establish cross-functional AI ethics committees and board-level oversight to ensure consistent, accurate AI disclosures across all materials.

• Investors should scrutinize AI claims in earnings calls and filings, distinguishing between substantive implementations and opportunistic marketing signals.

• Transparent AI disclosures require clearly defining capabilities, disclosing data sources and limitations, and maintaining consistent terminology across all communications.

Frequently Asked Questions

1. What are securities class actions, and why have they increased in the context of AI disclosures?

Securities class actions are lawsuits filed by groups of investors who allege they were harmed by a company’s misleading statements or omissions. The rise of AI disclosures has led to an increase in these actions, as investors target companies accused of exaggerating or misrepresenting their AI capabilities (a practice known as “AI-washing”).

2. How does securities litigation relate to inaccurate AI claims?

Securities litigation occurs when investors sue a company for making false or misleading statements, including those about AI claims. If a company overstates what its AI can do or fails to disclose limitations and risks, it may face legal action from shareholders seeking compensation for losses.

3. What is “AI-washing,” and how does it impact securities class actions?

“AI-washing” refers to the practice of overstating or misrepresenting the role and impact of artificial intelligence in a company’s products or services. This can trigger securities class action lawsuits if investors believe they were misled and suffered financial harm as a result.

4. What steps can companies take to reduce the risk of securities class actions related to AI disclosures?

Companies should ensure that all AI disclosures are accurate, consistent, and transparent across all filings. Establishing strong internal controls, clear governance structures, and consistent definitions for AI terms can help prevent allegations of misleading AI claims.

5. Why is transparency in AI disclosures important for avoiding securities litigation?

Transparent and precise AI disclosures help build investor trust and reduce the likelihood of securities litigation. By clearly communicating what their AI systems can—and cannot—do, companies minimize the risk of facing securities class actions due to alleged “AI-washing” or overstated claims.