AI incident #28 ·

2010 Market Flash Crash

What happened

A modified algorithm was able to cause dramatic price volatility and disrupted trading in the US stock exchange.

Only the incident metadata is stored here. The underlying news reports are on the AI Incident Database (CC BY-SA 4.0); use the links above to read them.

News reports (30)

Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.

  1. 2010 Flash Crash
    en.wikipedia.org · Wikipedia Editors · AIID #390
  2. Findings Regarding the Market Events of May 6, 2010
    sec.gov · U.S. Commodity Futures Trading Commission, U.S. Securities & Exchange Commission · AIID #401
  3. The 2010 'flash crash': how it unfolded
    theguardian.com · Jill Treanor, Philip Hensher · AIID #402
  4. What happened during the Flash Crash?
    telegraph.co.uk · Andrew Trotman · AIID #416
  5. Why the Cause of the 2010 Stock Market Flash Crash Really Matters
    nautil.us · Chris Clearfield, James Owen Weatherall · AIID #393
  6. 2010 Flash Crash Arrest Motivated By Greed
    seekingalpha.com · Seeking Alpha · AIID #412
  7. 'Flash Crash' a Perfect Storm for Markets
    graphics.wsj.com · Roger Kenny, Bradley Hope, Tynan DeBold · AIID #398
  8. "The flash crash" five years later
    marketplace.org · Pencer Platt Getty Images, Mitchell Hartman · AIID #405
  9. What actually caused 2010 "Flash Crash"
    businessinsider.com · Mark Melin · AIID #413
  10. 2010 Flash Crash
    corporatefinanceinstitute.com · Corporate Finance Institute · AIID #415
  11. The 2010 Flash Crash
    gffbrokers.com · GFF Brokers · AIID #399
  12. Herding and flash events: Evidence from the 2010 Flash Crash
    sciencedirect.com · Rıza Demirera, Karyl B. Leggio, Donald Lien · AIID #407
  13. Herding and Flash Events: Evidence From the 2010 Flash Crash
    papers.ssrn.com · Riza Demirer, Karyl Leggio, Donald D. Lien · AIID #400

Who was involved

Alleged harmed party
Market Participants

Classification (MIT AI Risk Repository taxonomy)

Causal entity
AI
Intent
Intentional
Timing
Post-deployment
Harm level
none
Sectors
financial and insurance activities
Countries

Risk entries describing this failure mode

Entries from the MIT AI Risk Repository coded to subdomain 4.3.

  • Impersonation/identity theft

    "Impersonation/identity theft - Theft of an individual, group or organisation’s identity by a third-party in order to defraud, mock or otherwise harm them."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • IP/copyright loss

    "IP/copyright loss - Misuse or abuse of an individual or organisation’s intellectual property, including copyright, trademarks, and patents."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Dehumanisation/objectification

    "Dehumanisation/objectification - Use or misuse of a technology system to depict and/or treat people as not human, less than human, or as objects."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Defamation/libel/slander

    "Defamation/libel/slander - Use of a technology system to create, facilitate or amplify false perception(s) about an individual, group, or organisation."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Financial and business

    "Financial and Business - Use or misuse of a technology system in a manner that damages the financial interests of an individual or group, or which causes strategic, operational, legal or financial harm to a business or...

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Cheating/plagiarism

    "Cheating/plagiarism - Use of another person’s or group’s words or ideas without consent and/or acknowledgement."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Cybersecurity

    "LLMs may exacerbate cybersecurity risks in various ways (Newman, 2024). Firstly, LLMs may significantly amplify the effectiveness of deceptive operations aimed at tricking people into disclosing sensitive information or...

    Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

  • Misinformation and Manipulation

    "Recent studies have demonstrated that LLMs can be exploited to craft deceptive narratives with levels of persuasiveness similar to human-generated content (Pan et al., 2023b; Spitale et al., 2023), to fabri- cate fake n...

    Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

Incidents in the same risk subdomain

All incidents in this subdomain