Ida B. Wells was a pioneer in the United States who used data collection and analysis methods well ahead of her time. Today, she might be called a data scientist, but I'll call her a boss!

Her approach to documenting and analyzing lynchings back in the day (late 19th century) demonstrated key elements of modern data science that directly relate to today's discussions around data ethics and responsible innovation. For instance:

Data management

Wells meticulously gathered data on hundreds of lynching cases across the South. She developed a comprehensive data set that included details such as dates, locations, victims' names and alleged crimes (all analog, no database, while under threat of physical harm).

Much like modern discussions of dataset transparency and completeness in AI training, Wells understood that the quality and comprehensiveness of input data directly affect the validity of conclusions drawn from it.

Statistical analysis

Wells analyzed patterns in her data that revealed reality versus prevailing narratives about lynching. She demonstrated through statistical analysis that the common justification for lynchings – allegations of rape – was false in the vast majority of cases.

In contemporary times, this is called algorithmic bias. It should be easy to see the importance of challenging automated systems that ingest false narratives as they perpetuate societal prejudices and often, as with lynching, offer no redress.

Data-driven narratives

In her works like Southern Horrors (1892) and A Red Record (1895), Wells combined powerful narratives with statistical evidence.

Just as responsible innovators emphasize the idea of "socio-technical," where quantitative metrics and qualitative human impact assessments are combined, Wells understood that numbers alone weren't enough – they needed to be contextualized within human stories and experiences.

Fact-checking and verification

Despite living in the analog "before times" with scarce resources, Wells didn't think fact-checking was "too hard." In fact, it was necessary for her credibility. She cross-referenced newspaper reports with eyewitness accounts and other sources to verify her data. She developed a rigorous methodology for confirming the details of each case, similar to modern data verification practices.

Deploying insights

Similar to today's white papers and research reports, Wells distributed her groundbreaking research, often with tables and statistical summaries, through pamphlets, books and the Chicago Tribune newspaper. She also effectively presented her findings as a public speaker, making her a "techie" who could communicate.

This commitment to simplicity and interpretability is akin to the push for explainability with AI systems. Systems whose decisions can be understood and scrutinized by the communities they affect garner more trust from those communities. Audience-specific, digestible insights helped make her arguments more compelling and harder to dismiss.

Wells' legacy and the fight for ethical innovation

Perhaps most importantly, Wells demonstrated that data science should serve humanitarian ends. She exposed the "lynch abomination now generally practiced against colored people in the South," as Frederick Douglass wrote about her work. She showed how rigorous data practices can expose injustice and instigate positive social change – a crucial lesson for today's AI community and society writ large as we grapple with questions of ethical deployment and the pace of innovation.

Regretfully, despite many attempts to urge political leaders like the President and Congress, Wells was unsuccessful in getting a repeal of the lynching laws her research condemned. This proves once again that technology (and data) is an enabler of our values.

The US Congress finally passed anti-lynching legislation in 2022. Wells would have been 160 years old.

Her legacy reminds us that the true measure of technical innovation isn't its sophistication but its capacity for justice and human well-being. Her work demonstrates how data science principles can be applied to create meaningful social change, making her a pioneer in civil rights activism and data-driven research.

If you enjoyed this post, be sure to check out more stories from SAS bloggers about data ethics.