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
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
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
We have already seen examples of this bias in real life, such as biased #facial recognition technology that disproportionately affects certain #ethnic groups
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
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
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
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
🚨 THREAD: "The New AI PMF Playbook is Hype. Here’s Why You Should Think Twice." OpenAI’s Product Lead dropped a flashy new “AI-native PMF framework” claiming to replace everything we know about Product-Market Fit. Everyone’s applauding. But here’s what no one’s saying... 🧵👇
Ive will now wizard a H/W form factor "no one ever saw coming" to pair ChatGPTs questionable GPT strategy with an all-encompassing ultra "iPOD" moment for the merged entity. Their motivation? It's exolained in the next post by them!
These groups have been known to carry out cyber espionage, intellectual property theft, and sabotage. For instance, the #FancyBear APT group was responsible for the alleged 2016 US election interference
The IEEE GLOBAL GENERAL PRINCIPLES OF ‘ETHICALLY ALIGNED DESIGN’ initiative on the ethics of autonomous & intelligent systems (A/IS) includes 8 pillars
1. HUMAN RIGHTS: AI shall be created & operated to respect, promote, & protect internationally recognized human rights
A real-world example of this pillar:
1. A facial recognition system used by law enforcement that respects individuals’ privacy and does not discriminate against certain groups
2. WELL-BEING: AI creators shall adopt increased human well-being as a primary success criterion
A real-world example is a healthcare AI system that prioritizes patient outcomes and improves overall well-being, rather than just maximizing profits
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