Plaintiff seeks to recover actual, compensatory, liquidated, and punitive damages for discrimination based upon discrimination concerning his disability. Plaintiff submits a request to the court for lost salary and benefits, future lost salary and benefits, and compensatory damages for emotional pain and suffering.
Discrimination With Ai In Massachusetts
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Multi-State
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US-000286
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Any person located within the commonwealth using a generative artificial intelligence system to create audio, video, text or print AI-generated content, or repurposing such content, shall be prohibited from removing any notice or disclosure, as provided in section 2.
Defective Products: AI would be treated as a product, and if it does not provide the safety or functionality expected, the developer may be held strictly liable for any losses caused, without the need to prove negligence.
If a deep creates a misleading impression about you that a reasonable person would find offensive, you may have a valid claim for false light. Like defamation, you must prove that the deep was widely published and that it portrays you in a misleading and harmful way.
If a decision made with the assistance of AI is found to be contrary to the best interests of the company or if the CEO fails to exercise reasonable care in utilizing AI tools, they could be held liable for breaching their fiduciary duty.
AI liability and current law Ultimately, liability for negligence would lie with the person, persons or entities who caused the damage or defect or who might have foreseen the product being used in the way that it was used.
While the AI user may have initiated the process, accountability could extend to the user's manager or the employing company who allowed such a situation to occur. AI developers and vendors, too, might face scrutiny for any deficiencies in the system's design that allowed the error.
The easiest way to prove unlawful discrimination is through the use of direct evidence. Direct evidence is the kind that, if believed, requires a conclusion that unlawful discrimination motivated the employer's decision. Direct evidence requires no inference or presumption.
Researchers and technologists have repeatedly demonstrated that algorithmic systems can produce discriminatory outputs. Sometimes, this is a result of training on unrepresentative data. In other cases, an algorithm will find and replicate hidden patterns of human discrimination it finds in the training data.
The MCAD enforces M.G.L. c. 151B which makes it illegal to discriminate against prospective or current employees on the basis of their membership in a protected class. Workplace harassment based on membership in a protected class is also prohibited.