How Predictim pressures babysitters to grant broad access to their social media, uses AI to analyze years of online activity to provide evaluations to parents
When Jessie Battaglia started looking for a new babysitter for her 1-year-old son, she wanted more information … Tweets: @justinhendrix , @hunterwalk , @katecrawford , @kierisi , @lydnicholas , @lmsacasas , @dhh , @dhh , and @dhh Tweets: Justin Hendrix / @justinhendrix : A company called Predictim is offering parents what tech firms are selling to employers: artificial-intelligence systems that analyze a person's speech, facial expressions & social media history to determine if a person would be a suitable babysitter.http://www.washingtonpost.co m/ ... @hunterwalk : Should make the parents consent to being scanned as well, and only let them hire sitters at same/lower score than their own http://twitter.com/... Kate Crawford / @katecrawford : Well yes, this AI sitter screening is error-prone, based on broken assumptions, and privacy invading. What's worse - it's a horrifying symptom of the growing power asymmetry between employers and job seekers. And low wage workers don't get to opt out. http://www.washingtonpost.com/ ... Jesse Mostipak / @kierisi : “...as Predictim's technology influences parents' thinking, it remains entirely [...] vulnerable to quiet biases over how an appropriate babysitter should share, look and speak.” I guarantee you there is nothing quiet about the race/class/gender biases in these systems. http://twitter.com/... Lydia Nicholas / @lydnicholas : Cripes this is horrific. Look; algorithms to support decisions in care and to help find good matches could be helpful but not a black box that forces you to smile for a machine interviewer, mines your social media and produces an unexplained numerical score of character. http://twitter.com/... LM Sacasas / @lmsacasas : Thesis: all such tools are symptoms and accelerators of the breakdown of trust and judgment that emerges organically within well-functioning, human-scale communities. http://twitter.com/... @dhh : You don't have to be a part of building this dystopian hellscape. Maybe you can find a way to excuse working at Facebook, but Predicitim is so beyond the pale that not even military-grade cognitive dissonance tolerance can excuse you. This is Terrible Ethics in Software 101. @dhh : Who can we thank for funding this ethically-deprived dumpster fire? Your friends at @UCBerkeley and their @SkyDeck_Cal fund. Putting science to work in dystopian science oh-my-god-it's-not fiction @dhh : Predictim's black-box algorithm analyzes babysitters' social media accounts, reducing them to a single fit score. Beyond disgusting. Downright evil. Social media algorithms prod you to be the worst/fake you can be, hiring algorithms reject you for it. https://www.washingtonpost.com/ ...
Context & Ripple Effects
Predictim takes the vetting tools employers use and points them at domestic childcare: babysitters are pressured to hand over broad social media access so an AI can reduce years of posts, speech, and facial expressions to an unexplained numerical fit score for parents. It is the household version of a pattern already documented in formal hiring, where algorithms sifting job applications were found to disadvantage poor applicants.
The backlash was immediate — commentators cited in the coverage, including Kate Crawford and other ethicists, attacked the service as privacy-invading and biased, and a follow-up Washington Post report found the character scores Predictim generates rest on questionable recommendations. The story matters because it moves algorithmic person-scoring out of HR departments and police budgets into the family living room.
First-order effects
- Babysitter applicants face a new gatekeeper: refusing broad social media access risks a lower or missing score, so privacy becomes a condition of getting childcare work.
- Parents like Jessie Battaglia get opaque numerical evaluations instead of references, while Predictim absorbs reputational damage from public criticism by ethicists and journalists.
Second-order effects
- Platforms holding the underlying data — Facebook among the named entities — face pressure over how their users' histories feed third-party scoring products they did not consent to.
- Consumer-facing rivals and background-check services must decide whether to match AI scoring or differentiate on transparency, since the coverage shows unexplained scores drawing the sharpest criticism.
Third-order effects
- If informal-economy screening normalizes, the same architecture already deployed in policing, sentencing, and probation per the New York Times coverage extends to everyday trust decisions, widening the gap between scored and unscored workers.
- The documented failure modes — bias in hiring algorithms and race/ethnicity detection that researchers warn fuels discrimination — point toward regulatory scrutiny of consumer AI scores, though whether rules arrive before adoption spreads is genuinely uncertain.
The trend: AI-driven person scoring is migrating from employment and criminal justice into intimate, informal decisions like childcare, dragging privacy and bias debates into the home.