مشکلات عینیت و سوگیری مرتبط با آن موضوع برجسته و تکراری در یافته­ های مطالعات مربوطه در هنگام تصمیم­گیری بوده است. به عنوان مثال، سان[1] (2019) در مورد کاربرد یادگیری عمیق در روش­های حسابرسی برای شناسایی اطلاعات و چالش­های آن براساس سوگیری به سختی قابل ردیابی و ساختارهای داده بسیار پیچیده می­نویسد. علاوه ­براین، آرنابولدی[2] و همکاران (2017) رسانه­ های اجتماعی و تغییر اطلاعات کلان داده و درنتیجه فرایندهای تصمیم­گیری مغرضانه را مورد بررسی قرار دادند. مطالعه لیچ-دیبالد[3] و همکاران (2019) نیز استفاده از هوش مصنوعی را در ارزیابی شغل و درخواست­های وام افراد توضیح می­دهد و شواهد زیادی از تبعیض را مطرح کرده است. از طرفی نیز سانچزمدینا[4] و همکاران (2017) تأثیر تغییر در هنجارها را بر وضعیت تداوم نگرانی براساس استفاده حسابرسان از هوش مصنوعی مطالعه کرد. با نگاهی عمیق­تر به زمینه­های موجود در مقالات، الگوریتم­های زیربنایی هوش مصنوعی و داده­های بزرگ به عنوان عوامل مؤثر در اکثر چالش­های اخلاقی برای تصمیم­گیری­های مبتنی بر هوش مصنوعی شناسایی شدند. برای مثال، این الگوریتم­ها برای پردازش وام­های افراد، هشدار در مورد از دست دادن اعتبار بالقوه و شناسایی الگوهای پرداخت دیده می­شوند (بومان[5] و همکاران، 2019؛ کلوگ[6] و همکاران، 2020).

با این حال، این الگوریتم­ها خروجی کار انسان هستند و داده ­های ارائه شده از گذشته سرچشمه می­گیرند و اغلب توسط انسان­ها انتخاب می­شوند. از این­رو پتانسیل سوگیری را نیز دارند. درنتیجه، به جای این­که بپرسیم آیا هوش مصنوعی می­تواند عینی باشد، سؤالات می­تواند بدین شرح باشد: چگونه انسان­ها می­توانند الگوریتم­های عینی بسازند؛ آیا داده­هایی که الگوریتم­ها را تغذیه می­کنند عاری از سوگیری ذاتی هستند. آموزش یک سیستم هوش مصنوعی برای نادیده گرفتن نژاد، جنسیت و گرایش جنسی و اتخاذ تصمیمات براساس اطلاعات دیگر امکان­پذیر است. از سوی دیگر، هوش مصنوعی همچنین می­تواند فرصت­هایی را برای غلبه بر تعصبات انسانی فراهم کند. همان­طور که داگرتی[7] و همکاران (2019) بیان کردند: اگر برنامه­ های نرم­افزاری می­توانستند نابرابری­هایی را که دسترسی اقلیت­ها به وام­های مسکن و سایر وام­ها را محدود کرده است، محاسبه کنند، چه می­شود؟ به بیانی دیگر، اگر به سیستم­های ما آموزش داده شود که داده­های مربوط به نژاد، جنسیت، گرایش جنسی و سایر ویژگی­هایی را که به تصمیمات موردنظر مرتبط نیستند نادیده بگیرند، چه اتفاقی می­افتد؟. با این وجود، چنین سیستمی تنها با کمک و آگاهی اخلاقی متخصصان انسانی که سیستم­های هوش مصنوعی را ایجاد و آموزش می­دهند، ایجاد می­شود (لهنر[8] و همکاران، 2020). بنابراین این چالش خاص عینیت عمدتاً بر مؤلفه­ های قضاوت اخلاقی و انگیزه اخلاقی در مدل رِست[9] تأثیر می­گذارد. زیرا هر دو با توجه به اطلاعات یا الگوریتم­های مغرضانه ناقص خواهند بود. بنابراین برای مثال از طریق دستورالعمل­های روشن و ایجاد آگاهی برای توسعه ­دهندگان و کارکنان، باید با آن برخورد شود.

[1] . Sun

[2] . Arnabouldi

[3] . Lietch-Debald

[4] . Sanchezmedina

[5] . Bumann

[6] . Kellogg

[7] . Daggerti

[8] . Lehner

[9] . Rest

 ادبیات چالش های اخلاقی.. 3

 عینیت.. 3

 امنیت.. 4

 شفافیت.. 6

 پاسخگویی.. 8

صداقت.. 10

 تعریف اخلاق و چگونگی ارتباط آن با هوش مصنوعی.. 13

 پیشینه‌ای در تصمیم‌گیری اخلاقی.. 13

پیامدهای اخلاقی.. 16

 پیشینه پژوهش... 16

 پیشینه داخلی.. 16

 پیشینه خارجی.. 18

منابع   21

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