Career Advancement Programme in Bias and Fairness in Machine Learning in Mandarin Chinese

Tuesday, 17 February 2026 13:19:35

International applicants and their qualifications are accepted

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Overview

Overview

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Entry requirements

The program operates on an open enrollment basis, and there are no specific entry requirements. Individuals with a genuine interest in the subject matter are welcome to participate.

International applicants and their qualifications are accepted.

Step into a transformative journey at LSIB, where you'll become part of a vibrant community of students from over 157 nationalities.

At LSIB, we are a global family. When you join us, your qualifications are recognized and accepted, making you a valued member of our diverse, internationally connected community.

Course Content

•   ?????????????? (Jiqì xuéxí zhong de biacha yu gongpíng xìng gàilùè)
•   ????????? (Biacha lèixíng yu jiancè fangfa)
•   ?????????? (Gongpíng xìng dùliàng zhibiao yu pínggu)
•   ????????? (Huanjie suànfa biacha de jìshù)
•   ?????????? (Shùjù yù chuli yu tèzheng gongchéng)
•   ???????????? (Yinguo tuiduàn zài gongpíng xìng zhong de yìngyòng)
•   ????????????? (Jiqì xuéxí zhong de gongpíng xìng falu fagui)
•   ????:?????????? (Ànlì fenxi: biacha yu gongpíng xìng ànlì yánjiu) **(Translation of Units):** * Overview of Bias and Fairness in Machine Learning * Types of Bias and Detection Methods * Fairness Metrics and Evaluation * Techniques for Mitigating Algorithmic Bias * Data Preprocessing and Feature Engineering * Application of Causal Inference in Fairness * Laws and Regulations on Fairness in Machine Learning * Case Studies: Bias and Fairness Case Studies These units cover a broad range of topics crucial for understanding and addressing bias and fairness in machine learning. The keywords "?? (biacha)" (bias) and "??? (gongpíng xìng)" (fairness) are prominently featured.

Assessment

The evaluation process is conducted through the submission of assignments, and there are no written examinations involved.

Fee and Payment Plans

30 to 40% Cheaper than most Universities and Colleges

Duration & course fee

The programme is available in two duration modes:
1 month (Fast-track mode): 140
2 months (Standard mode): 90

40% Cheaper

Our course fee is up to 40% cheaper than most universities and colleges.

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Awarding body

 The programme is awarded by London School of International Business. This program is not intended to replace or serve as an equivalent to obtaining a formal degree or diploma. It should be noted that this course is not accredited by a recognised awarding body or regulated by an authorised institution/ body.

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  • Start this course anytime from anywhere.
  • 1. Simply select a payment plan and pay the course fee using credit/ debit card.
  • 2. Course starts
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Got questions? Get in touch

Chat with us: Click the live chat button

+44 75 2064 7455

admissions@lsib.co.uk

+44 (0) 20 3608 0144



Career path

?? (Position) - ????????? (Machine Learning Bias and Fairness) ?? (Description)
??????? (Machine Learning Engineer) - ??????? (Bias Detection and Mitigation) ???????,???????????????????????,????
????? (Data Scientist) - ????? (Fairness Evaluation) ????????????,?????????????????????,?????
???????? (AI Ethicist) - ???? (Algorithmic Accountability) ????????????????,??????????????,??????
????? (Software Engineer) - ??????? (Fairness Tool Development) ????????????????????????????,?????

Key facts about Career Advancement Programme in Bias and Fairness in Machine Learning in Mandarin Chinese

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本职业发展项目旨在提升学员在机器学习中偏差与公平性方面的专业技能，帮助其成为该领域的专家。课程内容涵盖偏差检测、缓解策略以及公平性评估等多个方面，学员将学习如何构建更公平、更可靠的机器学习模型。


通过学习，学员将掌握先进的偏差检测技术，例如数据分析和模型解释方法；了解并运用各种缓解偏差和促进公平性的策略，例如数据预处理、算法选择和后处理技术；最终能够独立评估机器学习模型的公平性，并撰写相关的技术报告。


项目时长为三个月，包含线上课程、实践项目和行业专家指导。学员将参与实际案例分析，解决实际问题，积累宝贵的经验，为未来的职业发展奠定坚实基础。 项目紧密结合产业需求，课程内容由行业专家设计，确保学员掌握最新的技术和实践方法，使其能够快速适应工作环境。


该职业发展项目在人工智能算法、机器学习伦理、数据科学等领域具有极高的行业相关性。毕业学员将在科技公司、金融机构、医疗保健等行业拥有广阔的职业发展前景，并为企业解决人工智能应用中面临的伦理和社会责任挑战做出贡献。 掌握该技能的专业人士在市场上拥有很高的需求， 这也是一个极具发展潜力的职业方向。


参与本《机器学习偏差与公平性职业发展项目》将使您在竞争激烈的就业市场中脱颖而出，成为人工智能领域备受瞩目的专业人才。 我们提供专业的职业指导，帮助您将所学知识转化为实际的职业成就。

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Why this course?

职业发展规划在当今机器学习领域中的公平性和偏差问题上至关重要。英国的数据显示，算法偏差对少数族裔群体的影响日益严重。根据英国政府2023年的一份报告，人工智能系统中存在的偏见导致了就业机会的不平等分配。例如，在招聘过程中，基于机器学习的筛选系统可能对女性和少数族裔申请人存在偏见，导致他们获得面试机会的概率降低。

为了解决这个问题，职业发展规划应包含专门针对公平性和偏差的模块，重点关注算法的公正性评估、数据偏差的识别和纠正以及多元化团队的构建。这不仅能提升机器学习模型的可靠性，更能促进一个更加公平公正的工作环境。 英国国家统计局的数据显示，2022年，在科技行业工作的女性比例仅为17%，这凸显了在职业发展规划中纳入多元化和包容性培训的必要性。

Group Percentage
Women in Tech (UK, 2022) 17%
Minorities in Tech (UK, 2022) 12%
White Males 65%

Who should enrol in Career Advancement Programme in Bias and Fairness in Machine Learning in Mandarin Chinese?

???? (Ideal Learners) ?? (Characteristics)
??????? (Machine Learning Engineers) ??????????????????????,??????,????????AI??????,???????????,???????????????????
????? (Data Scientists) ?????????????????,???????????????????? ???????????????????????????
???? (Product Managers) ???????????????????????????,???????????????????????????????????
???????? (Technical Leads & Management) ????????????????????,???????AI????,???????????????????????