Fears for shop workers grow as AI automation gains speed

Trade unions have urgently called for tighter regulation on artificial intelligence in the workplace to prevent workers being hired and fired by algorithms.

Companies have been increasingly looking to AI to streamline HR practices during the pandemic, the Financial Times reported.

A report from the Trades Union Congress (TUC) has determined that employment regulations have been moving at a much slower pace than the roll out of new tech in the workplace.

Autonomous technology is being used in recruitment processes where candidates facial movements and words are analysed and fed back to the employer to help with their decision.

The report found that in one case, a longstanding worker was dismissed due to an AI absence system which didn’t process her doctor’s note correctly, a manager at the hearing then deemed the system was correct.

READ MORE: Skyrocketing delivery costs are forcing retailers to accelerate change through AI

The TUC has called for governments and regulators to introduce new worker’s rights laws which would mean any decision taken by an algorithm requires human review to avoid unfair or discriminatory decisions.

“Without fair rules, the use of AI at work could lead to widespread discrimination and unfair treatment.” general secretary of the TUC Frances O’Grady said

One of the lawyers commissioned to write the report Robin Allen said: “Increasingly employers are not doing things, it’s the machines doing it,”

“Where you substitute a boss with an algo, you’re undermining the personal relationship. Employers must never forget the personal relationship.”

Prospect, professional’s trade union member Andrew Pakes, argued: “I’m quite deaf. I always look like I’m gurning on a call. Does that make it look like I’m not very interested?”

Uber Eats were highlighted for criticism in the report as recent workers have claimed they were dismissed unfairly as a result of facial recognition software which was found to be unreliable when used with employees from ethnic minority backgrounds.

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