AI and Machine Learning in Government Briefing
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This briefing document discusses opportunities and challenges around using artificial intelligence and machine learning. It identifies two potential topics: using AI to detect spear phishing attempts and avoid ransomware incidents, and using AI to automate help desk applications. The document provides background on AI and machine learning. It discusses examples of how the private sector uses AI and considers whether those uses could benefit government. It identifies both potential benefits and risks/challenges around using AI, such as workforce disruption, data and privacy issues, and governance challenges. The document concludes with recommendations to cautiously test new AI capabilities in pilots while also piloting governance approaches, leverage existing AI features, and periodically update on progress.
AI and Machine Learning in Government Briefing
- 1. Opportunities and Challenges Briefing Artificial Intelligence and Machine Learning Andrew Maier, Timothy Jones
- 2. TOPIC #1 Identify potential services or automated processes for Agencies to better detect spear phishing attempts and avoid ransomware incidents. Identify potential services or automated processes for Agencies to better detect spear phishing attempts and avoid ransomware incidents.
- 3. Leverage AI for automated Help Desk applications. Identify tools and methods for triaging service requests and inquiries. TOPIC #2 Leverage AI for automated Help Desk applications. Identify tools and methods for triaging service requests and inquiries.
- 4. Artificial Intelligence is software that mimics human behavior so well as to be indistinguishable from a person.
- 5. Machine learning is software that improves its own task performance using training data.
- 6. Artificial Intelligence (AI) Machine Learning (ML)
- 7. Timeline Machine learning 2004 ? Artificial intelligence 2012 2017 20XX Speech ImageText Cognition
- 8. JPMorgan Chase introduced a system for reviewing commercial loan contracts (COIN); work that used to take loan officers 360,000 hours/yr can now be done in a few seconds.” Clear potential “
- 9. Other Examples ● Infosys ○ Systems and Network Management ● BP ○ BP Wellfinder ● Ping An Insurance ○ Claim resolution and settlement
- 10. But how useful is it for government?
- 11. ● Likely not good for defending against spear phishing. ○ Government isn’t as targeted as the private sector. ○ Multiple people recommended YubiKey (hardware). ● Likely very good for defending against ransomware. ○ ML is well suited to protect against static threats like viruses, ransomware, and spam detection.
- 12. ● Help-desks present tradeoffs in the short/long term. ○ Good News: Already work in motion: ■ USA.gov’s AI Project ■ GSA’s AI Working Group ■ USCIS Virtual Asst. “Emma” ■ Contact Center of Excellence ○ Bad News: ■ Automating tier-1 support presents a paradox.
- 13. ● AI Raises numerous infrastructure considerations. ○ Training Data=> Storage costs ○ Processing => GPU premium expenses ○ Network Traffic/Upgrades ○ “Where are the customers’ yachts?” ● AI might create new opportunities ○ Automated “white glove service” DevOps ○ Automated, predictive outbound communications to reduce inbound calls.
- 14. ● Workforce disruption -“Human-Machine Relations” ● Data ● Privacy ● Compliance Governance issues
- 15. Workforce Disruption ● What happens to the existing people? ● How to integrate remaining people and machines? ○ “Manufacturing Safety” ■ There’s no shut-off switch. ● What are the ethics of AI? ○ New Google Deepmind Ethics Group ● No Asimov’s 3 Laws of Robotics
- 16. Data Issues ● Naked Algorithms vs. Training Data ● Quality ○ Need for “Failure” training data ● Scarcity ● Ownership ● Fragmentation ○ Remember “Islands of Information”?
- 17. Privacy ● Regulatory boundaries ○ Financial ○ Medical/Health (HIPAA) ○ Ethnographic ● Opt-In rules ○ “Should I have to agree to being AI’ed, or is the assumption that I don’t and have to Opt-out” ○ Shanghai CCTV
- 18. Compliance ● There’s no auditability ● There’s no reporting ● There’s no transparency ○ “Where’s the console?” ● No AI stack interoperability ○ Beware balkanization of AI Stack
- 19. Summary and Recommendations ● Cautious optimism. Test new capabilities in pilots. ● Conduct governance pilots in parallel with AI/ML pilots. ● Leverage existing features (such as Salesforce’s “Einstein”). ● Be a “Fast Follower” to private sector efforts. ● Periodic updates from work on the ground.
- 20. Discussion/Q&A