MACHINE learning and artificial intelligence are increasingly part of daily life. This emerging technology can improve processes and make them more accurate. That said, it can also exacerbate existing inequities by embedding unconscious biases of human designers and using data generated in inequitable systems.
Machine learning is the process by which information is introduced to a computer to identify patterns using algorithms. The patterns recognized are called models, which describe the information methodically, allowing predictions about the world beyond initial data. In circumstances where the product is artificial intelligence, the computer can continuously learn as it performs tasks based on initial models.
For example, if the objective is to learn about people’s purchasing preferences, providing data on purchase histories to the computer can allow it to identify patterns in similar purchases via grouping algorithms. It can create models of preferences and ultimately recommend similar items to individuals when they search for merchandise next time.
Even in this oversimplification, two sources of algorithmic bias emerge: the original data and the algorithm, both created by humans. So what impacts how fair or biased the technology is?
1. Limitations of the evidence base
The first phase of limiting bias in the technology is understanding the context in which you operate. Often algorithms include assumptions derived from research and literature. There is an inherent assumption that science is definitive, objective, and unbiased, however, science is a process. Building solid foundations of evidence and building its evidence base requires multiple studies on various groups representative of the population by different researchers, showing similar results over time. Unfortunately, diversity, equity and fairness have historically not been prioritized, and a limited evidence base is truly generalizable to everyone. That is why it is crucial to critically evaluate the existing literature for comprehensiveness, fairness and applicability before applying any assumptions.
For example, pharmaceutical clinical trials have mainly recruited adult white males; however, the results are generalized to the entire population. Algorithms built upon these results often do not accurately represent all underrepresented groups and can lead to unintentionally biased technologies.
2. Limitations of the data sets and algorithms
In the second phase, limitations of 1) the data from which the computer will learn and 2) the algorithms being used should be considered. While these seem detailed questions meant for data scientists, the decision’s impact on the result is immense; therefore, business segments need to at least be aware of the methods and able to communicate the results with limitations in mind.
For example, credit scores are determined through models that attempt to capture financially risky behaviours and poor habits. However, marginalized groups have historically been offered predatory products that lead to the snowballing of debt, which is then reflected in the data. This example is one of many that amplify systemic bias in the data via models and continues to disadvantage one population while advantaging another, especially through a scoring system that is used so frequently and ubiquitously.
Understanding the context and limitations and tempering the interpretation of the results is an important step that should involve the business unit and company leaders.
3. Impacts of new technology on marginalized communities
The negative impact of tech and algorithmic biases may not be intentional; however, the solution to health inequities should be. Therefore, in the third phase, development and business segments must be diverse, and multidisciplinary and include subject matter experts and community stakeholders to contextualize the results and think critically about possible outcomes. Additionally, it is crucial to assess the post-market, real-world evidence after the technologies have been used for some time and are transparent and accountable for those outcomes.
For example, there are race-based algorithms for medical decision-making. Ideally, the algorithms lessen disparities, although historically, this has not been the case. For instance, there are models which estimate the kidney’s filtration rate to determine if patients require specialized treatment or qualify for transplants. Previously, the model would predict higher filtration rates for Black patients (i.e. they would appear less ill).
Unfortunately, the resulting policies led to an increased rate of disease progression and delayed referrals for transplantation for Black individuals. One study estimates that if the models were still used, approximately 68,000 Black adults would not be referred to specialist care (population estimates were calculated based on the Diao et al publication and the American Community Survey ACSDT5Y2020 dataset). At the same time, an additional 16,000 would be ineligible for the transplant waiting list. This outcome is particularly undesirable considering the incidence of End-Stage Kidney Disease is three times higher in Black individuals compared to white.
Steps could have been taken earlier to prevent inequitable care from worsening after these clinical practices were implemented but that’s not the overarching theme. The point is that the algorithm’s application and potential impact were neither considered thoroughly nor contextualized within the epidemiology and inequity of kidney failure.
Beyond algorithmic biases
As data science becomes a prevalent tool, organizations should ensure that those technologies don’t simply automate existing algorithm biases. By working through these three key considerations, having multidisciplinary teams and staying accountable after the technology is in use, organizations can help mitigate algorithmic bias, prevent exacerbation of existing inequities and help create a more equitable environment.
Most importantly, the ubiquity of data science across all industries is here to stay. Therefore, to maximize its use for transformative outcomes and avoid beginner missteps, all companies should invest in better understanding of data science at every level.
World Economic Forum
Thu Sep 08 2022
![Erasing bias in emerging technologies - 3 considerations Erasing bias in emerging technologies - 3 considerations](https://resizer-awani.eco.astro.com.my/tr:w-177,h-100,q-100,f-auto/https://img.astroawani.com/2022-09/81662112804_Artificialntelligenc.jpg)
Machine learning and artificial intelligence can dramatically improve health; however, they still require human inputs which can introduce unconscious and systemic biases. - ETX Studio
Tumpuan Isnin – 08 Julai 2024
Ikuti rangkuman berita utama yang menjadi tumpuan di Astro AWANI.
'Jeff' dinobat siput terpantas dunia, menang Kejohanan Dunia Lumba Siput
Tahun ini, siput bernama Jeff dinobatkan sebagai pemenang, menang saingan akhir dengan masa 4 minit dan 3 saat. Begitupun, kepantasan Jeff tidak dapat memadam rekod yang dibuat pada tahun 1995 oleh seekor siput bernama Archie.
![Pentadbiran Prabowo-Gibran dijangka perkukuh industri sawit Indonesia Pentadbiran Prabowo-Gibran dijangka perkukuh industri sawit Indonesia](https://resizer-awani.eco.astro.com.my/tr:w-177,h-100,q-100,f-auto/https://img.astroawani.com/2024-07/81720368598_TBPrabowo.jpg)
Pentadbiran Prabowo-Gibran dijangka perkukuh industri sawit Indonesia
Kerajaan baharu yang akan dipimpin oleh Prabowo Subianto-Gibran Rakabuming Raka dijangka memperkukuh daya saing produk minyak sawit sebagai komoditi strategik untuk pasaran domestik dan antarabangsa.
![Kanak-kanak lelaki maut selepas terjatuh ke dalam tasik Kanak-kanak lelaki maut selepas terjatuh ke dalam tasik](https://resizer-awani.eco.astro.com.my/tr:w-177,h-100,q-100,f-auto/https://img.astroawani.com/2024-01/61706067730_TBgarisanpolis.jpg)
Kanak-kanak lelaki maut selepas terjatuh ke dalam tasik
Seorang kanak-kanak lelaki berusia sembilan tahun maut selepas terjatuh ke dalam tasik dipercayai ketika sedang mencari lokan di Taman Tasik Teratai Fasa 2 Serendah, Hulu Selangor.
Ribuan umat Islam zahir solidariti kepada Palestin di Shah Alam
Bertemakan Gaza Bangkit, program pada Ahad malam itu turut mengundang pendakwah dari Indonesia, Ustaz Abdul Somad Batubara untuk menyampaikan ceramah.
NGO, belia perlu ambil iktibar peristiwa hijrah - PM
Perdana Menteri, Datuk Seri Anwar Ibrahim menyeru pertubuhan bukan kerajaan dan gerakan belia mengambil iktibar daripada peristiwa Hijrah Nabi Muhammad SAW untuk mengembalikan kegemilangan negara.
Kehadiran ATM di Timur Sabah berikan keyakinan masyarakat setempat
Kehadiran pasukan Angkatan Tentera Malaysia (ATM) di Timur Sabah meningkatkan keyakinan masyarakat setempat serta merancakkan lagi pembangunan sektor pelancongan, ekonomi dan sosial di kawasan itu.
![Berita tempatan pilihan sepanjang hari ini Berita tempatan pilihan sepanjang hari ini](https://resizer-awani.eco.astro.com.my/tr:w-177,h-100,q-100,f-auto/https://img.astroawani.com/2023-12/61703329565_TBBERITAx.jpg)
Berita tempatan pilihan sepanjang hari ini
Berikut adalah berita yang paling menjadi tumpuan sepanjang Ahad, 7 Julai 2024.
![Berita antarabangsa pilihan sepanjang hari ini Berita antarabangsa pilihan sepanjang hari ini](https://resizer-awani.eco.astro.com.my/tr:w-177,h-100,q-100,f-auto/https://img.astroawani.com/2024-03/71709993909_TBBERITAx.jpg)
Berita antarabangsa pilihan sepanjang hari ini
Berikut adalah yang paling menjadi tumpuan sepanjang Ahad, 7 Julai 2024.
Enam cedera hari pertama Festival Larian Lembu di Sepanyol
Meskipun ada yang cedera, acara tersebut sentiasa mendapat perhatian dan tahun ini, ratusan orang mengejar enam ekor lembu jantan di bandar itu.
![Arab Saudi guna AI untuk kurangkan kesesakan Arab Saudi guna AI untuk kurangkan kesesakan](https://resizer-awani.eco.astro.com.my/tr:w-177,h-100,q-100,f-auto/https://img.astroawani.com/2024-06/81718415400_jalanraya.jpg)
Arab Saudi guna AI untuk kurangkan kesesakan
Penyelesaian AI digunakan bagi membantu memperhebat pelbagai sistem, antaranya pemantauan trafik masa nyata dan sistem kawalan isyarat adaptif.
![AI seperti ‘tsunami’ beri kesan besar kepada pasaran buruh global - Ketua IMF AI seperti ‘tsunami’ beri kesan besar kepada pasaran buruh global - Ketua IMF](https://resizer-awani.eco.astro.com.my/tr:w-177,h-100,q-100,f-auto/https://img.astroawani.com/2024-05/41715634875_KristalinaGeorgieva.jpg)
AI seperti ‘tsunami’ beri kesan besar kepada pasaran buruh global - Ketua IMF
AI beri kesan kepada 60 peratus pekerjaan dalam ekonomi maju dan 40 peratus pekerjaan di seluruh dunia dalam tempoh dua tahun akan datang.
![Mark Zuckerberg bincang risiko AI dengan PM Jepun Mark Zuckerberg bincang risiko AI dengan PM Jepun](https://resizer-awani.eco.astro.com.my/tr:w-177,h-100,q-100,f-auto/https://img.astroawani.com/2018-03/41521772351_MarkZuckerberg.jpg)
Mark Zuckerberg bincang risiko AI dengan PM Jepun
Ketua Meta dan pengasas Facebook sangat teruja dengan perkembangan yang berlaku di Jepun.
![Transformasi kejayaan Microsoft pimpinan satu dekad Satya Nadella Transformasi kejayaan Microsoft pimpinan satu dekad Satya Nadella](https://resizer-awani.eco.astro.com.my/tr:w-177,h-100,q-100,f-auto/https://img.astroawani.com/2023-02/41675851843_SatyaNadella.jpg)
Transformasi kejayaan Microsoft pimpinan satu dekad Satya Nadella
10 tahun berlalu sejak Satya Nadella menggalas tanggungjawab sebagai Ketua Pegawai Eksekutif (CEO), Microsoft.
![AI akan ubah kehidupan manusia dalam masa lima tahun, kata Bill Gates AI akan ubah kehidupan manusia dalam masa lima tahun, kata Bill Gates](https://resizer-awani.eco.astro.com.my/tr:w-177,h-100,q-100,f-auto/https://img.astroawani.com/2023-06/51686895515_billgates.jpg)
AI akan ubah kehidupan manusia dalam masa lima tahun, kata Bill Gates
Bill Gates percaya sejarah menunjukkan setiap teknologi baharu hadir ketakutan dan menyusul dengan peluang baharu.
![Kefahaman jelas bantu rakyat terima teknologi AI Kefahaman jelas bantu rakyat terima teknologi AI](https://resizer-awani.eco.astro.com.my/tr:w-177,h-100,q-100,f-auto/https://img.astroawani.com/2024-01/61705455723_TBAIjpg.jpg)
Kefahaman jelas bantu rakyat terima teknologi AI
Ketidakfahaman masyarakat kepada konteks sebenar kepentingan teknologi itu mencetus kepada persepsi AI sebagai musuh.
![Memahami metaverse, superkomputer dan AI Memahami metaverse, superkomputer dan AI](https://resizer-awani.eco.astro.com.my/tr:w-177,h-100,q-100,f-auto/https://img.astroawani.com/2022-02/71644246629_teknologi.jpg)
Memahami metaverse, superkomputer dan AI
Metaverse ialah detik penting dalam dunia realiti lanjutan, dunia yang menghampiri 'kehidupan kedua' yang telah lama diramalkan.
![PLUS perkenal 'PUTRI', chatbot pertama perkhidmatan lebuh raya PLUS perkenal 'PUTRI', chatbot pertama perkhidmatan lebuh raya](https://resizer-awani.eco.astro.com.my/tr:w-177,h-100,q-100,f-auto/http://img.astroawani.com/2020-05/81590120687_BERNAMA.jpg)
PLUS perkenal 'PUTRI', chatbot pertama perkhidmatan lebuh raya
Syarikat konsesi lebuh raya PLUS Malaysia Berhad (PLUS) memperkenalkan chatbot pertama bagi mengendalikan maklumbalas mengenai perkhidmatan lebuh raya secara digital iaitu 'PUTRI'.
![Artificial Intelligence bantu pantau data peribadi pengguna FB Artificial Intelligence bantu pantau data peribadi pengguna FB](https://resizer-awani.eco.astro.com.my/tr:w-177,h-100,q-100,f-auto/http://img.astroawani.com/2018-04/61523489557_TBMARKZUCKERBERGHEA.jpg)
Artificial Intelligence bantu pantau data peribadi pengguna FB
Mark Zuckerberg terus menjawab soalan daripada anggota Kongres pada hari kedua pendengaran kes perbicaraannya berhubung kebocoran maklumat Cambridge Analytica.
![China aims to enable half of all new cars with AI by 2020 China aims to enable half of all new cars with AI by 2020](https://resizer-awani.eco.astro.com.my/tr:w-177,h-100,q-100,f-auto/http://img.astroawani.com/2018-01/41515148364_trafficJem.jpg)
China aims to enable half of all new cars with AI by 2020
China is aiming to become a world leader in artificial intelligence by 2025, with an aim to grow its core AI industries to over US$22.15 billion by 2020.