Makro İktisadi Perspektiften Yapay Zekâ ve Yoksulluk İlişkisi Küresel Fırsatlar, Riskler ve Politika Önerileri
Öz
Anahtar Kelimeler
Kaynakça
- Acemoğlu, D. (2021). Redesigning AI: Work, democracy and justice in the age of automation. In K. Crawford, R. Calo, & R. Lee (Eds.), Boston Review Forum: Redesigning AI. Boston Review.
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- Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction machines: The simple economics of artificial intelligence. Harvard Business Review Press.
- Aijaz, N., Lan, H., Raza, T., Yaqub, M., Iqbal, R., & Pathan, M. S. (2025). Artificial intelligence in agriculture: Advancing crop productivity and sustainability. Journal of Agriculture and Food Research, 20, 101762.
- Aiken, E., Bellue, M., Karlan, D., Udry, C., & Blumenstock, J. (2022). Machine learning and phone data can improve targeting of humanitarian aid. Nature, 603(7903), 864–870.
- Alonso, C., Kothari, S., & Rehman, S. (2020). How artificial intelligence could widen the gap between rich and poor nations (IMF Working Paper No. 2020/184). International Monetary Fund.
- Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine bias: There’s software used across the country to predict future criminals. And it’s biased against Blacks. ProPublica.
- Blumenstock, J. E., Cadamuro, G., & On, R. (2015). Predicting poverty and wealth from mobile phone metadata. Science, 350(6264), 1073–1076.
Ayrıntılar
Birincil Dil
Türkçe
Konular
Sürdürülebilir Kalkınma
Bölüm
Araştırma Makalesi
Yazarlar
Yunus Budak
*
0000-0003-3126-743X
Türkiye
Erken Görünüm Tarihi
2 Aralık 2025
Yayımlanma Tarihi
19 Aralık 2025
Gönderilme Tarihi
10 Mayıs 2025
Kabul Tarihi
25 Temmuz 2025
Yayımlandığı Sayı
Yıl 2025 Cilt: 10 Sayı: 2