Predictive Policing Inequality Auditor

Inspired by the stark social divides in Metropolis and the AI-driven justice systems hinted at in Hyperion, and leveraging the data-scraping focus of the 'AI Workflow for Companies' project, the 'Predictive Policing Inequality Auditor' is designed to address algorithmic bias in predictive policing.

The Story: Imagine a future where law enforcement relies heavily on AI to predict crime hotspots. Just like in Metropolis, this system disproportionately targets certain communities, leading to over-policing and exacerbating existing inequalities. Citizens, aware of the potential for bias, seek a tool to hold the system accountable.

The Concept: The project involves developing a web-based tool that allows users to input data related to a specific predictive policing system. This data might include historical crime statistics, demographic information, and the algorithm's prediction outputs (if available, or simulated based on common algorithms). The tool then uses statistical analysis and machine learning techniques to identify potential biases in the algorithm's predictions. Specifically, it assesses whether the algorithm is more likely to flag certain demographic groups or geographic areas, even after accounting for other factors.

How it works:
1. Data Input: The user inputs relevant data, which can be scraped from publicly available crime statistics or, in the absence of real data, simulated based on common predictive policing models. A dataset of 'fair' or 'ground truth' crime could be generated via simulations to compare and contrast the bias of existing models.
2. Bias Detection: The tool analyzes the data to identify potential biases. It checks for disparities in prediction accuracy across different demographic groups and geographic areas. Metrics like disparate impact, equal opportunity, and predictive parity are used to quantify bias.
3. Visualization and Reporting: The results are presented in an easy-to-understand format, with charts and graphs highlighting any identified biases. A report is generated summarizing the findings, including specific recommendations for mitigating the biases.

Implementation: The project can be implemented using Python with libraries like scikit-learn, pandas, and matplotlib for data analysis and visualization. Flask or Django can be used to create the web interface. The 'AI Workflow for Companies' scraper inspiration could be used to scrape relevant public datasets for analysis.

Niche & Low Cost: It's a niche tool addressing a specific problem (algorithmic bias in predictive policing), can be developed with open-source tools, and focuses on publicly available data.

High Earning Potential: The tool can be offered as a service to civil rights organizations, law firms, government agencies, and community groups who are concerned about algorithmic bias. It can also be used by companies that are developing predictive policing systems to ensure that their algorithms are fair and unbiased. Subscription-based access, consulting services, and custom report generation can all generate revenue.