This algorithm could predict your health, income, and chance of premature death

Holly Barker

Science.org

Originally published 18 DEC 23

Here is an excerpt:

The researchers trained the model, called “life2vec,” on every individual’s life story between 2008 to 2016, and the model sought patterns in these stories. Next, they used the algorithm to predict whether someone on the Danish national registers had died by 2020.

The model’s predictions were accurate 78% of the time. It identified several factors that favored a greater risk of premature death, including having a low income, having a mental health diagnosis, and being male. The model’s misses were typically caused by accidents or heart attacks, which are difficult to predict.

Although the results are intriguing—if a bit grim—some scientists caution that the patterns might not hold true for non-Danish populations. “It would be fascinating to see the model adapted using cohort data from other countries, potentially unveiling universal patterns, or highlighting unique cultural nuances,” says Youyou Wu, a psychologist at University College London.

Biases in the data could also confound its predictions, she adds. (The overdiagnosis of schizophrenia among Black people could cause algorithms to mistakenly label them at a higher risk of premature death, for example.) That could have ramifications for things such as insurance premiums or hiring decisions, Wu adds.

The info is here.

Here is my summary:

A new algorithm, trained on a mountain of Danish life stories, can peer into your future with unsettling precision. It can predict your health, income, and even your odds of an early demise. This, achieved by analyzing the sequence of life events, like getting a job or falling ill, raises both possibilities and ethical concerns.

On one hand, imagine the potential for good: nudges towards healthier habits or financial foresight, tailored to your personal narrative. On the other, anxieties around bias and discrimination loom. We must ensure this powerful tool is used wisely, for the benefit of all, lest it exacerbate existing inequalities or create new ones. The algorithm’s gaze into the future, while remarkable, is just that – a glimpse, not a script. 

Mitigating welfare-related prejudice and partisanship among U.S. conservatives with moral reframing of a universal basic income policy

Thomas, C. C., Walton, G. M., et al.

Journal of Experimental Social Psychology

Volume 105, March 2023, 104424

Abstract

Inequality and deep poverty have risen sharply in the US since the 1990s. Simultaneously, cash-based welfare policies have frayed, support for public assistance has fallen on the political right, and prejudice against recipients of welfare has remained high. Yet, in recent years Universal Basic Income (UBI) has gained traction, a policy proposing to give all citizens cash sufficient to meet basic needs with no strings attached. We hypothesized that UBI can mitigate the partisanship and prejudice that define the existing welfare paradigm in the US but that this potential depends critically on the narratives attached to it. Indeed, across three online experiments with US adults (total N = 1888), we found that communicating the novel policy features of UBI alone were not sufficient to achieve bipartisan support for UBI or overcome negative stereotyping of its recipients. However, when UBI was described as advancing the more conservative value of financial freedom, conservatives perceived the policy to be more aligned with their values and were less opposed to the policy (meta-analytic effect on policy support: d = 0.36 [95% CI: 0.27 to 0.46]). Extending the literatures on moral reframing and cultural match, we further find that this values-aligned policy narrative mitigated prejudice among conservatives, reducing negative welfare-related stereotyping of policy recipients (meta-analytic effect d = −0.27 [95% CI: −0.38 to −0.16]), while increasing affiliation with them. Together, these findings point to moral reframing as a promising means by which institutional narratives can be used to bridge partisan divides and reduce prejudice.

Highlights

• Policies like Universal Basic Income (UBI) propose to mitigate poverty and inequality by giving all citizens cash

• A UBI policy narrative based in freedom most increased policy support and reduced prejudice among conservatives

• This narrative also achieved the highest perceived moral fit, or alignment with one’s values, among conservatives

• Moral reframing of policy communications may be an effective institutional lever for mitigating partisanship and prejudice

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General discussion

Three experiments revealed that a values-based narrative of UBI, one grounded in the conservative value of economic freedom, can advance bipartisanship in support for UBI and simultaneously mitigate welfare-related prejudice among U.S. conservatives. While policy reforms often focus on changes to objective policy features, these studies suggest that the narratives attached to such features will meaningfully influence public attitudes towards both the policy and its recipients. In other words, the potential of policies like UBI to advance goals such as inequality reduction and prejudice mitigation may be limited if they fail to attend to the narratives that accompany them.

Here, we demonstrate the potential for policy narratives that elevate the moral foundations of those most opposed to the policy, U.S. conservatives in this case. Why might this narrative approach succeed? At a higher-order level, our findings suggests that inclusion begets inclusion: when conservatives felt that the policy recognized and reflected their own values, they were more likely to support the policy and express inclusive attitudes toward its recipients.

The research is here.