Data Analysis & Critical Thinking - Flawed data leads to flawed actions, impacting companies, workplaces, governments, and institutions [6] - Critical thinking about data is essential for inclusive, empowering, and accurate actions [7] - Data gaps occur when information is missed or the right questions aren't asked to the right people [9] - Confirmation bias and pre-existing beliefs can skew the interpretation of data [13][14] - Biases in data creation and sharing can lead to skewed actions [14][17] - Overwhelming volume of data can lead to reliance on easily accessible but inaccurate data points [18] Data Manipulation & Misinformation - Disinformation involves deliberately manipulating data to serve specific interests [25] - Media moguls can significantly impact narratives and public perception [26] - AI can amplify existing data flaws and create new ones [31] Examples of Data Flaws - The myth that carrots provide night vision originated from a British propaganda campaign [3][4] - Car crash test mannequins primarily based on male body proportions lead to higher injury rates for women (40-70% more likely to be moderately to seriously injured) [7][8][10] - A 2014 report incorrectly stated 3,000 kidnappings in Nigeria when the actual number was around 300 [20] - Human trafficking affects 40 to 50 million people, representing 05% of the global population [22]

AI Bias and Inclusion Gaps - AI systems rely on the data they are trained on, and unseen or unheard voices are not reflected in the AI mirror [2] - 90% of data used to train AI systems is in English, while only 18% of the world speaks English [6] - Only 1% of the data used in large language models for global AI models is African language data [7] - Globally, only 20% of AI professionals are women, and in the UK, only 1.7% of AI professionals are black [7] - CV filtering algorithms show bias, with white-sounding names having an 85% chance of moving to the next stage compared to 9% for black-sounding names [9] - Facial recognition systems have higher error rates for darker-skinned women (34.7%) compared to lighter-skinned men (0.08%) [10] Impact of AI Bias - Biased AI systems can affect hiring decisions, medical treatment, credit scoring, and content streaming [8] - Exclusion in AI development leads to scaled exclusion, while equity promotes inclusion and opportunity [12][13] Addressing AI Bias and Promoting Inclusion - Digital exclusion leads to data exclusion; in Africa, only 40% have internet access, and in the Caribbean, as low as 11% [15][16] - Global standards and policies are needed to protect, value, and respect data, treating data breaches with the same seriousness as money laundering [18] - Companies should disclose the inclusive characteristics of their data, similar to nutritional information on food packaging [19] - Diversity of thought in AI is a skills gap that governments must address by encouraging the participation of women and black people in AI development [20][21]