Thread by @Graham_dePenros

As a society, we must ensure that the #AI systems we are building are #inclusive and #equitable. This will only happen through increased transparency and #diversity in the field. Using already "dirty data" is not the way

https://twitter.com/CryptoPalantir/status/1614619775098920960

Using biased data to train AI has serious consequences, particularly when data is controlled by large corporations with little #transparency in their training methods

For fair & #equitable AI we need Web3 democratized & agendaless data for AI training

https://twitter.com/theanswer_web3/status/1614716198226108418

The use of flawed #AI training datasets propagates #bias, particularly in #GPT-type models which are now widely hyped but are controlled by compromised #Web2 MNCs who have a poor track record in #privacy, protecting civil #liberty & preserving free speech

mishcon.com/news/new-claim…

We have already seen examples of this bias in real life, such as biased #facial recognition technology that disproportionately affects certain #ethnic groups

sitn.hms.harvard.edu/flash/2020/rac…

Additionally, the lack of transparency in the training data and methods used by corporations makes it difficult to detect and address bias in AI systems

brookings.edu/research/algor…

The development of explainable AI #XAI is not keeping pace with advancements in AI, making it harder to understand the #blackbox nature of AI decisions

As a society, we must demand that investment in #ExplainableAI keeps pace with the dev of #AGI & AI

engineering.dynatrace.com/blog/understan…

This lack of transparency and #ethics in AI decision-making has significant consequences, particularly in areas such as #healthcare and #finance

ncbi.nlm.nih.gov/pmc/articles/P…

Lack of diversity in data scientists, engineers & researchers working on AI contributes to #bias in AI

Biased AI systems perpetuate & amplify existing societal inequalities & #discrimination, leading to the further #marginalization of certain groups

theconversation.com/artificial-int…

It's crucial we address these issues to ensure fair & #ethical AI development by increasing transparency in training data & methods, plus diversifying teams. By fostering more diverse & inclusive teams, we have a chance to create a more robust & fair AI

computerweekly.com/opinion/Why-di…

There are efforts in industry & academia to detect & mitigate bias in AI systems & make them fairer, but we need more

The problem of bias in AI is complex, but by taking a holistic approach & addressing it at every stage of the AI dev process, we can create an ethical AI future

As a #society, we need to ensure that the AI systems we are building are #inclusive and #equitable

This will only happen through increased transparency and diversity in the field