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skills/action-items-from-client-alert/examples/example_vague_alert.md

Worked Example — Vague Alert

This example demonstrates Action Items from Client Alert handling a vague alert that contains substantial informational content but few extractable action items. The skill produces a brief report and explicitly notes that the alert is informational rather than actionable.

Input

Alert date: 2026-04-30 Review date: 2026-05-07 Document: Law firm client alert titled "FTC Signals Heightened Focus on Algorithmic Decision-Making — What Companies Should Know"

Optional inputs:

  • organization_context: "Mid-market consumer financial services company; uses ML models for credit decisions; subject to ECOA, FCRA."
  • relevant_business_areas: "consumer protection, ML/AI use, credit decisioning"
  • applicable_jurisdictions: "US federal"

Document (excerpts):

CLIENT ALERT — FTC Signals Heightened Focus on Algorithmic Decision-Making

April 30, 2026

Recent FTC enforcement actions and statements from senior FTC officials suggest the Commission is increasing its focus on algorithmic and automated decision-making systems, particularly in consumer-facing contexts. While the FTC has not announced new rules specifically addressing algorithmic decision-making, the agency has indicated through several channels that companies using such systems should expect heightened scrutiny.

Recent Developments:

  • In a March 2026 enforcement action against [Company], the FTC alleged that the company's automated screening system produced disparate outcomes that violated Section 5 of the FTC Act and the ECOA. The settlement included a $X million civil penalty and required ongoing monitoring of the company's algorithmic systems.

  • In April 2026 remarks, FTC Chair [name] emphasized that "the FTC's existing authorities are well-suited to address algorithmic harm" and that "companies cannot hide behind algorithms to avoid responsibility for outcomes that violate consumer protection laws."

  • The FTC's June 2025 staff report on algorithmic decision-making (cited in the recent remarks) outlined principles companies should consider, including transparency, auditability, fairness testing, and human oversight.

Implications for Companies:

Companies using algorithmic decision-making in consumer-facing contexts should consider the following:

  • Whether their existing compliance programs adequately address algorithmic risks.
  • Whether their model documentation supports the demonstration of fair and accurate outcomes.
  • Whether their human oversight mechanisms are sufficient to detect and correct disparate outcomes.
  • Whether their consumer disclosures appropriately describe the role of algorithms in decision-making.

The FTC has not announced new rules. However, companies should expect that the agency will continue to use its existing authorities — Section 5, ECOA, FCRA, and others — to address algorithmic harms. We will continue to monitor developments in this area.

Looking Ahead:

Industry participants should also be aware that:

  • Several state attorneys general have indicated similar interest in algorithmic decision-making, particularly in California and New York.
  • The CFPB has issued related guidance on algorithmic decision-making in consumer financial contexts.
  • The EU AI Act, while not directly applicable to US-only operations, reflects the international trend toward heightened algorithmic accountability.

This alert is for general informational purposes only and is not legal advice.

Output

Action Items from Client Alert: FTC Focus on Algorithmic Decision-Making

Source document: Client Alert dated April 30, 2026 — "FTC Signals Heightened Focus on Algorithmic Decision-Making" Alert date: April 30, 2026 Organization context: Mid-market consumer financial services company; uses ML models for credit decisions; subject to ECOA, FCRA Relevant business areas: Consumer protection, ML/AI use, credit decisioning Applicable jurisdictions: US federal

Context summary

The alert describes the FTC's signaled increase in focus on algorithmic decision-making systems based on a March 2026 enforcement action and April 2026 remarks from the FTC Chair. Critically, the FTC has not announced new rules; the alert is reporting an enforcement-priority shift, not a new requirement. For the user (consumer financial services with ML credit decisioning), the alert is highly relevant context but does not impose specific deadlines or new obligations.

Mandatory action items

None. The alert does not describe any new mandatory requirements with deadlines.

The user's existing obligations under Section 5 of the FTC Act, ECOA, and FCRA continue to apply. The FTC's signaled focus may increase enforcement risk but does not change the underlying substantive obligations.

1. Review existing compliance program for algorithmic risk coverage

What: assess whether the user's existing compliance program adequately addresses algorithmic decision-making risks, including documentation, fairness testing, human oversight, and consumer disclosures.

Why recommended: the alert's "Implications for Companies" section identifies these as areas of FTC focus. While not legally required by this alert, the FTC's increased enforcement attention makes this a prudent review.

Owner: Legal (regulatory) coordinated with Compliance, Risk Management, and Data Science / ML teams.

Source citation: alert's "Implications for Companies" section.

Applicability: Applicable to user given ML use in credit decisioning.

2. Review model documentation for fair-outcomes demonstration

What: confirm model documentation can support demonstration of fair and accurate outcomes if challenged by the FTC, CFPB, or state attorneys general.

Why recommended: the March 2026 enforcement action cited in the alert involved disparate outcomes from automated screening; documentation supporting fairness testing and outcome monitoring would be central to defending similar challenges.

Owner: Legal (regulatory), Compliance, Data Science / ML teams.

Source citation: alert's "Implications for Companies" section, citing the March 2026 enforcement action.

Applicability: Applicable to user.

3. Review human oversight mechanisms for detection and correction of disparate outcomes

What: confirm that human oversight processes can detect disparate outcomes from ML credit decisions and enable correction.

Why recommended: the alert's principles include human oversight; the FTC's posture is that algorithms do not absolve companies of substantive obligations.

Owner: Risk Management coordinated with Data Science / ML teams; Legal review.

Source citation: alert's "Implications for Companies" section.

Applicability: Applicable to user.

4. Review consumer disclosures regarding role of algorithms

What: review consumer disclosures (adverse action notices, application disclosures, terms of service) to ensure appropriate description of algorithmic role in decision-making.

Why recommended: consumer-facing disclosures are a focus area in the alert. Existing ECOA / FCRA disclosure obligations may already cover much of this; the review is to confirm adequacy under heightened FTC focus.

Owner: Legal (regulatory, consumer compliance) coordinated with Marketing and Operations.

Source citation: alert's "Implications for Companies" section.

Applicability: Applicable to user given consumer financial services context and ECOA / FCRA applicability.

Informational items

  • March 2026 FTC enforcement action against [Company]: monitoring this case (settlement details, ongoing monitoring requirements) could provide insight into FTC's algorithmic enforcement methodology.
  • June 2025 FTC staff report on algorithmic decision-making: referenced in the alert and informs the FTC's principles. Worth obtaining for the legal team's reference file.
  • State attorneys general interest: California and New York are mentioned as showing similar interest. State-level enforcement is also possible.
  • CFPB related guidance: CFPB has issued related guidance on algorithmic decision-making in consumer financial contexts. Particularly relevant to the user given consumer financial services context. Obtain for compliance file.
  • EU AI Act trend: mentioned as international context; not directly applicable to US-only operations.

Items where applicability is unclear

None — all items are applicable given the user's organization context.

Items not applicable to user's organization

None.

Source references and follow-ups

  • Underlying authorities: Section 5 of the FTC Act, ECOA, FCRA — all already applicable to user.
  • Cited materials:
    • March 2026 FTC enforcement action against [Company] — obtain settlement details for compliance file.
    • April 2026 FTC Chair remarks — obtain text for context.
    • June 2025 FTC staff report on algorithmic decision-making — obtain for reference.
    • CFPB algorithmic decision-making guidance — obtain for compliance file.
  • Suggested follow-ups:
    • Schedule periodic monitoring of FTC enforcement actions in algorithmic decision-making.
    • Consider engaging outside counsel for assessment of model fairness documentation if internal review identifies gaps.
    • Review whether existing model risk management framework (if any) needs enhancement to address algorithmic-specific FTC concerns.

Notes on this extraction

The alert is informational rather than actionable. It describes an enforcement priority shift, not a new legal requirement. The "recommended action items" extracted are responses to the prudential concerns raised in the alert; they are recommended for risk management purposes but are not legally required by this alert.

If the user is looking for a new compliance roadmap, this alert does not provide one. The FTC has not issued new rules; existing FTC Act, ECOA, and FCRA obligations continue to apply. For substantive compliance assessment of the user's ML credit decisioning systems against existing law, the user should consider a separate engagement with regulatory counsel — this alert is a signal that such an engagement may be increasingly important, not a substitute for it.

What this example demonstrates

  • Vague alerts produce brief reports. No mandatory action items extracted; the report is shorter than for a regulation-implementing alert. The skill does not pad with speculation.
  • Recommended action items are differentiated from mandatory. All four items in the report are recommended (best practice in light of FTC enforcement focus); none are legally required by this alert. The structural distinction matters for the user's prioritization.
  • Context summary is candid about the alert's nature. "The FTC has not announced new rules; the alert is reporting an enforcement-priority shift" — this honesty helps the user calibrate how to respond. A user who expected a new compliance deadline now knows there isn't one.
  • Notes on extraction explicitly states "informational rather than actionable." This is important — vague alerts can be misleading if treated as if they impose requirements. The skill is honest about what extraction produced.
  • Recommendation to engage regulatory counsel is appropriate. When the alert is a signal rather than a roadmap, the right next step is often expert legal analysis tailored to the user's specific operations. The skill recommends this rather than fabricating a detailed roadmap from limited source content.
  • Informational items capture useful context without inflating to actions. Five informational bullets describe related developments; none are converted to actions because the alert doesn't support that.
  • Source references include both legal authority and adjacent materials. The cited materials (enforcement action, staff report, Chair remarks, CFPB guidance) are flagged for compliance file building — useful operational guidance even when the alert itself doesn't impose deadlines.