Bill Overview
Title: Algorithmic Accountability Act of 2022
Description: This bill requires certain businesses that use automated decision systems to make critical decisions to study and report about the impact of those systems on consumers. Critical decisions include those that have a significant effect on a consumer's life such as the cost or availability of health care, housing, educational opportunities, or financial services. The Federal Trade Commission (FTC), in consultation with relevant stakeholders, must issue regulations to implement the bill. The bill provides for enforcement by the FTC and specified state officials. Further, the bill establishes a Bureau of Technology to advise the FTC about the technological aspects of its functions.
Sponsors: Rep. Clarke, Yvette D. [D-NY-9]
Target Audience
Population: Individuals affected by automated decision systems in healthcare, housing, education, or financial services
Estimated Size: 25000000
- The bill impacts individuals subjected to automated decision systems.
- These systems cover critical decisions related to health care, housing, education, and financial services.
- A large proportion of the global population is involved in these sectors in some way, either as recipients of services or consumers.
- Technological advancement has led to widespread adoption of automated systems in various sectors globally.
- Globally, housing, healthcare, and financial services are active for most adults.
Reasoning
- The Algorithmic Accountability Act of 2022 predominantly impacts large sections of the population who interact with automated decision systems across critical sectors.
- Since the US population is approximately 330 million, with high tech adoption rates, it is estimated that about 25 million individuals are substantially impacted.
- Due to budget constraints, the policy can focus on larger businesses that significantly influence decision-making in healthcare, housing, education, and financial services.
- Given the large number of people impacted, the policy might only influence a minority significantly in the short term, while over time, increased accountability could improve outcomes such as reduced biases, increased fairness, and general satisfaction.
Simulated Interviews
software engineer (New York, NY)
Age: 28 | Gender: female
Wellbeing Before Policy: 6
Duration of Impact: 5.0 years
Commonness: 12/20
Statement of Opinion:
- I think it's about time systems are held accountable. I've had experiences where automated systems seemed biased, like credit scoring.
- If businesses are more transparent about these algorithms, it could lead to improved decision-making and fairness.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 6 | 6 |
| Year 2 | 7 | 6 |
| Year 3 | 7 | 6 |
| Year 5 | 8 | 6 |
| Year 10 | 8 | 6 |
| Year 20 | 8 | 6 |
real estate agent (Los Angeles, CA)
Age: 45 | Gender: male
Wellbeing Before Policy: 5
Duration of Impact: 3.0 years
Commonness: 15/20
Statement of Opinion:
- Automation in housing has sped things up but often lacks human nuance, which can be frustrating.
- Getting more clarity on how these systems work is a good step, though I'm unsure if it will fix the core frustrations for real estate professionals.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 5 | 5 |
| Year 2 | 5 | 5 |
| Year 3 | 6 | 5 |
| Year 5 | 6 | 5 |
| Year 10 | 6 | 5 |
| Year 20 | 6 | 5 |
retired teacher (Dallas, TX)
Age: 63 | Gender: female
Wellbeing Before Policy: 7
Duration of Impact: 2.0 years
Commonness: 10/20
Statement of Opinion:
- I hope this new policy brings improvements to healthcare systems, as they often seem too automated and impersonal.
- I appreciate the focus on decision-making, though it might not affect me immediately as a retiree.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 7 | 7 |
| Year 2 | 7 | 7 |
| Year 3 | 7 | 7 |
| Year 5 | 7 | 7 |
| Year 10 | 7 | 7 |
| Year 20 | 7 | 7 |
bank loan officer (Chicago, IL)
Age: 34 | Gender: male
Wellbeing Before Policy: 6
Duration of Impact: 7.0 years
Commonness: 13/20
Statement of Opinion:
- I support any policy that grants more transparency and accountability in automated credit systems.
- It might lead to more fairness, but the big change will depend on how strictly it's enforced and whether it leads to real improvements.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 6 | 6 |
| Year 2 | 7 | 6 |
| Year 3 | 7 | 6 |
| Year 5 | 8 | 6 |
| Year 10 | 8 | 6 |
| Year 20 | 8 | 6 |
college student (Seattle, WA)
Age: 22 | Gender: other
Wellbeing Before Policy: 5
Duration of Impact: 10.0 years
Commonness: 14/20
Statement of Opinion:
- Automated systems have made applying for loans a bit of a black box process. Having them held accountable sounds good to me.
- As someone just starting to navigate these systems, knowing they'll be more transparent is reassuring.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 5 | 5 |
| Year 2 | 6 | 5 |
| Year 3 | 6 | 5 |
| Year 5 | 6 | 5 |
| Year 10 | 7 | 5 |
| Year 20 | 7 | 6 |
small business owner (Miami, FL)
Age: 50 | Gender: male
Wellbeing Before Policy: 8
Duration of Impact: 1.0 years
Commonness: 9/20
Statement of Opinion:
- I see both pros and cons to automated systems, but clearer rules and accountability could be beneficial.
- I doubt it will have much immediate impact on my day-to-day operations.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 8 | 8 |
| Year 2 | 8 | 8 |
| Year 3 | 8 | 8 |
| Year 5 | 8 | 8 |
| Year 10 | 8 | 8 |
| Year 20 | 8 | 8 |
finance consultant (Boston, MA)
Age: 39 | Gender: female
Wellbeing Before Policy: 7
Duration of Impact: 5.0 years
Commonness: 8/20
Statement of Opinion:
- This policy could change how my clients approach automation strategies, potentially skewing them towards more accountable uses.
- I foresee some benefits to broader business environments, which might improve my consultancy work long-term.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 7 | 7 |
| Year 2 | 7 | 7 |
| Year 3 | 8 | 7 |
| Year 5 | 8 | 7 |
| Year 10 | 8 | 7 |
| Year 20 | 8 | 7 |
data analyst (San Francisco, CA)
Age: 30 | Gender: other
Wellbeing Before Policy: 6
Duration of Impact: 4.0 years
Commonness: 10/20
Statement of Opinion:
- My role involves checking these systems, so more scrutiny and regulations could streamline processes across the board.
- As a professional, it may increase the pressure for adequate compliance from my firm, but that's a positive change.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 6 | 6 |
| Year 2 | 7 | 6 |
| Year 3 | 7 | 6 |
| Year 5 | 7 | 6 |
| Year 10 | 7 | 6 |
| Year 20 | 7 | 6 |
HR manager (Austin, TX)
Age: 26 | Gender: female
Wellbeing Before Policy: 6
Duration of Impact: 6.0 years
Commonness: 12/20
Statement of Opinion:
- It's a necessary move considering how much bias can infiltrate these systems if unchecked.
- I expect long-term benefits in making hiring systems more equitable, even if changes won’t be immediate.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 6 | 6 |
| Year 2 | 6 | 6 |
| Year 3 | 7 | 6 |
| Year 5 | 7 | 6 |
| Year 10 | 7 | 6 |
| Year 20 | 7 | 6 |
healthcare administrator (Houston, TX)
Age: 55 | Gender: male
Wellbeing Before Policy: 8
Duration of Impact: 10.0 years
Commonness: 11/20
Statement of Opinion:
- Ensuring responsible use of automated systems in healthcare is critical—anything promoting accountability is beneficial.
- It could alleviate potential public backlash against automated healthcare decisions, which is always a concern.
Wellbeing Over Time (With vs Without Policy)
| Year | With Policy | Without Policy |
|---|---|---|
| Year 1 | 8 | 8 |
| Year 2 | 9 | 8 |
| Year 3 | 9 | 8 |
| Year 5 | 9 | 8 |
| Year 10 | 9 | 8 |
| Year 20 | 9 | 8 |
Cost Estimates
Year 1: $50000000 (Low: $40000000, High: $70000000)
Year 2: $55000000 (Low: $45000000, High: $75000000)
Year 3: $60500000 (Low: $49500000, High: $82500000)
Year 5: $72500000 (Low: $59500000, High: $99000000)
Year 10: $90000000 (Low: $75000000, High: $122000000)
Year 100: $200000000 (Low: $150000000, High: $250000000)
Key Considerations
- Rapid technological changes may alter the scope and nature of enforcement and regulatory requirements.
- Potential pushback from industry stakeholders opposed to increased regulation might affect implementation timelines.
- Coordination between multiple government levels and entities will be necessary to avoid redundancy and inefficiency.
- International developments and similar policies abroad might influence domestic implementation.