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Aiプロジェクトリスクマネジメント(上級)大学院修了証 (Advanced)

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Overview

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Learning outcomes

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Course content

1

Aiリスク評価の理論

2

プロジェクトリスクフレームワーク

3

データ品質とリスク

4

アルゴリズム透明性

5

法規制とコンプライアンス

6

倫理的aiガバナンス

7

ステークホルダー分析

8

リスク定量化手法

9

シナリオプランニング

10

リスク対応戦略

11

継続的モニタリング

12

インシデント管理プロセス

13

Aiシステム安全性評価

14

プライバシー保護技術

15

サプライチェーンリスク

16

ビジネスインパクト評価

17

コミュニケーションと報告

18

組織文化とリスク意識

19

高度なリスクシミュレーション

20

修了プロジェクト実装

Career Path

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Key facts

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Why this course

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There are no formal entry requirements for this course. You just need:

  • A good command of English language
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  • Basic computer skills
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Assessment is done through:

  • Multiple-choice questions at the end of each unit
  • You need to score at least 60% to pass each unit
  • You can retake quizzes if needed
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Upon successful completion, you will receive:

  • A digital certificate from London School of Business and Administration
  • Option to request a physical certificate
  • Transcript of completed units
  • Certification is included in the course fee
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Why people choose us for their career

Trusted by professionals worldwide

Verified outcomes from learners who finished the course and put it to work.

4.5
Based on 4 learner reviews · 4 countries
98%
Would recommend
100%
Verified learners
2026
Cohort active
Completed from United Kingdom
ST
Sarah Thompson
GB · Course completed

I loved the practical vibe of this course. The lessons on building an AI risk register were spot‑on – I actually used the spreadsheet they provided to log the risks for a chatbot rollout at my startup. The video demos made complex concepts like model‑explainability easy to grasp. While the workload was a bit heavy, the support from the Stanmore School of Business tutors kept me motivated. By the end I could confidently run a risk‑assessment workshop with senior stakeholders, which impressed my boss and got us green‑lighted for the next funding round.

MC
Michael Carter
US · Course completed

The Aiプロジェクトリスクマネジメント(上級)大学院修了証 course exceeded my expectations. The modules on AI‑specific risk identification gave me a clear framework to map out data‑drift and model‑bias risks. I was able to apply the risk‑heat‑map template directly to my company’s predictive‑maintenance project, which reduced unexpected downtime by 12 %. The reading materials are up‑to‑date, referencing the latest ISO/IEC 42001 standards, and the case studies from real‑world AI deployments helped me bridge theory and practice. Overall, the learning experience was rigorous yet supportive, and I feel fully equipped to lead AI risk‑management initiatives at my organization.

HT
Haruka Tanaka
JP · Course completed

この上級コースは本当に刺激的でした!AIプロジェクトのリスクマトリクス作成方法を学んだおかげで、社内の画像認識システムに対して具体的なバイアス対策を実装できました。特に、実践的なシミュレーション演習で、Monte Carlo法を使ってリスクの確率分布を可視化したのが印象的です。教材は最新の研究と業界ベストプラクティスがバランス良く組み込まれており、学びがすぐに仕事に活かせました。全体的に大満足で、次のAIプロジェクトでもこの知識を活用したいです。

ZD
Zanele Dlamini
ZA · Course completed

The course offered a detailed, step‑by‑step guide to AI risk governance that I could directly apply to a financial‑services AI model we were piloting. I especially appreciated the deep dive into regulatory compliance – the module on GDPR‑aligned risk documentation helped us draft a compliance checklist that passed our internal audit. The provided case study on a South African health‑tech AI deployment highlighted cultural considerations, which broadened my perspective. The blend of scholarly articles, hands‑on labs, and peer discussions made the learning experience thorough and engaging.





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Recently updated!

May 2026