.
.
See lessMains Answer Writing Latest Questions
PranjaliParmarBegginer
-
This answer was edited.
-
Generative Adversarial Networks (GANs) are a type of artificial intelligence used to generate new, realistic data based on existing data. They consist of two parts: the generator and the discriminator. The generator creates fake data, such as images, while the discriminator evaluates whether the datRead more
Generative Adversarial Networks (GANs) are a type of artificial intelligence used to generate new, realistic data based on existing data.
They consist of two parts: the generator and the discriminator. The generator creates fake data, such as images, while the discriminator evaluates whether the data is real or fake. These two parts work against each other in a continuous loop.
The generator tries to improve its fake data to fool the discriminator, while the discriminator gets better at identifying fake data. Over time, the generator becomes so skilled that the fake data looks very realistic.
This process can be visualized as a competition where both the generator and discriminator keep improving their skills. GANs are used in various fields, including art creation, image enhancement, and the development of realistic simulations.
See less
Several ethical implications are raised when it comes to deploying AI systems in a decision-making process. Biases and fairness are major concerns since AI systems may further enhance or perpetuate biases already present in data used for the training of such systems, hence hazardous and discriminatoRead more
Several ethical implications are raised when it comes to deploying AI systems in a decision-making process. Biases and fairness are major concerns since AI systems may further enhance or perpetuate biases already present in data used for the training of such systems, hence hazardous and discriminatory in their decisions. It requires rigorous testing, bias mitigation strategies, and a diverse set of data.
The other critical issue is that of transparency. Most AI systems are “black boxes” that don’t make it easy for one to understand their decision-making. There is, therefore, a problem of transparency that might undermine trust and accountability. Inclusion of explainable AI techniques might help improve this by making the processes of decision-making transparent.
Another major concern is privacy. Most AI systems require huge amounts of data, which raises concerns about the safety of data and chances of data misapplication. Strict measures of data protection and giving clear consent protocols are thus very critical to the safeguarding of user privacy.
Accountability is another key issue with regard to the dispensation of AI. Should something go wrong, as may be the case many times, laying accountability on somebody can be very difficult. Clear guidelines and accountability frameworks constituted for this are a must.
Last but not least, one should consider the impact on jobs and well-being in society. AI systems could displace jobs, causing the larger socioeconomic disparities between groups of people if managed improperly. Strategies relating to workforce transition and the fair distribution of benefits must form part of any ethical AI deployment.
In these ways, concerns about ethical implications can help businesses ensure that AI is responsibly and equitably applied during decision-making.
See less