Robert De Niro Loses Sexism Lawsuit Brought By Ex-Assistant, Company Must Pay Her $1.2M
Robert De Niro’s Production Company Ordered to Pay Ex-Assistant $1.2 Million in Sexism Lawsuit
Graham Chase Robinson, a former assistant to Robert De Niro, has been awarded $1.2 million in damages after winning a sexism lawsuit against De Niro’s production company, Canal. The verdict includes two identical payments of $632,142.96 for gender discrimination and retaliation. Initially seeking $12 million, Robinson claimed that she was unable to find a new job after being let go from De Niro’s employment and blamed him for it.
In response, De Niro counter-sued Robinson for $6 million, accusing her of stealing airline miles. However, she was found not liable. Robinson expressed her joy after the verdict was read, smiling at the jurors as they left the courtroom. Her lawyer, David Sanford, stated, “We are delighted that the jury saw what we saw and returned a verdict in Chase Robinson’s favor against Robert De Niro’s company.”
De Niro’s attorney, Richard Schoenstein, commented on the settlement amount, saying, “It strikes me as a compromise. Obviously they were seeking $12 million and they got $600,000. We are really happy they separated out Bob from this. It is a dispute between an employee and their former employer.”
Robinson began working for De Niro’s company in 2008 and accused him of verbally abusing her and treating female employees differently. She claimed that female employees were expected to be on call 24/7 while male employees were not. Robinson’s psychiatrist testified that she suffered from psychological conditions due to the trauma of losing her job and reputation, while a psychiatrist hired by De Niro’s lawyers described her as “narcissistic and paranoid.”
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The development process of this AI involved several key steps to ensure its reliability and accuracy. Here is a high-level overview of the process:
1. Problem Definition: The developers clearly defined the problem they sought to solve with the AI. They identified the specific tasks or goals the AI should be capable of achieving.
2. Data Collection: A large and diverse dataset was gathered, which served as the input for training the AI. This dataset was carefully curated to represent the real-world scenarios the AI would encounter.
3. Data Preprocessing: The collected data was cleaned and preprocessed to remove noise, errors, and inconsistencies. This step is crucial to ensure the AI can learn effectively from the data without being misled by irrelevant or incorrect information.
4. Model Selection: The developers chose an appropriate AI model architecture that best suited the problem at hand. This model could have been a pre-existing one or a customized solution built from scratch.
5. Training: The AI model was trained using the preprocessed dataset. This involved feeding the data into the model and optimizing its internal parameters to minimize errors and improve performance. Iterative processes like backpropagation and gradient descent were commonly used to fine-tune the model.
6. Validation and Testing: The trained AI model was evaluated using separate datasets that were not used during training. This allowed the developers to assess its performance, identify any shortcomings, and make necessary adjustments to improve accuracy and reliability.
7. Iterative Refinement: Based on the validation and testing results, the developers refined and optimized the AI model. This iterative process involved tweaking the model architecture, adjusting hyperparameters, and incorporating feedback from domain experts.
8. Deployment and Monitoring: Once the AI model demonstrated satisfactory performance, it was deployed for real-world use. During deployment, the system was continuously monitored to identify any issues or biases that might arise and ensure ongoing reliability and accuracy.
Throughout the development process, rigorous quality assurance and testing methodologies were applied to identify and rectify any flaws or biases in the AI system. Ethical considerations, such as fairness, transparency, and accountability, were also taken into account to ensure the AI’s reliability and accuracy aligned with ethical standards.
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