Day 12 — 25 分鐘口說練習

今日主題：Algorithmic bias and fairness

【HF】高頻內容詞（12 個）：show, reflect, cause, produce, address, prevent, reveal, detect, improve, ensure, measure, reduce

【BUNDLE】高頻詞組（3 個）：

• Research shows that…
• The problem is that…
• In other words…

0–3 分鐘：校準句

句子：In my view, the main POINT is simple, but the details matter.

標註：In my VIEW / the main POINT is SIMple / but the deTAILS MATter.

要點：

• 功能詞弱讀：in /ɪn/, my /maɪ→mɪ/, the /ðə/, is /ɪz→z/, but /bət/
• 句尾 MATter 下降終止，明確收束
• but 前後形成邊界（前段半終止、後段終止）

三遍法：

1. 第 1 遍：慢速，只求重音位置正確
2. 第 2 遍：正常語速，加上自然節奏
3. 第 3 遍：錄音一次

3–9 分鐘：音段對立（2 句）

句 1（/θ/–/ð/ 對立）

句子：【BUNDLE】Research 【HF】SHOWS that algorithms can 【HF】REFLECT the biases present in their training data, thus reproducing discrimination.

標註：ReSearch SHOWS / that ALgorithms / can reFLECT / the BIases PREsent / in their TRAINing DAta / THUS / reproDUcing discrimiNAtion.

操作化目標：

• that /ðæt/ 濁音 vs. thus /θʌs/ 清音 — 舌尖位置相同，送氣差異需明確
• their /ðeər/ 濁音，訓練成組出現 /ð/ 音
• that、in、their 弱讀
• that_algorithms、in_their 連結
• discrimiNAtion 句尾核重音並下降終止

句 2（/θ/–/ð/ 強化）

句子：【HF】FACE recognition systems 【HF】PERFORM worse on people with darker skin, and this 【HF】RAISES ethical concerns about fairness.

標註：FACE recoGNItion SYStems / perFORM worse / on PEOple with DARker skin / and THIS / RAIses Ethical conCERNS / about FAIRness.

操作化目標：

• this /ðɪs/ 濁音清楚，不可省略
• with /wɪð/ 濁音（非 /wɪθ/）
• ethical /ˈeθɪkəl/ 的 /θ/ 需清晰
• on、with、about 弱讀
• on_people、with_darker 連結
• FAIRness 句尾核重音並下降終止

9–15 分鐘：超音段焦點與對比（2 句）

句 3（數字對比）

句子：ProPublica found that the COMPAS algorithm had a false positive rate of forty-five percent for Black defendants, versus twenty-three percent for white defendants.

讀法 A（強調黑人被告的錯誤率）：

• 標註：ProPUBLica found / that the COMpas ALgorithm / had a false POsitive rate / of FORTY-five perCENT / for Black deFENdants / versus twenty-three percent for white defendants.
• 核重音在 FORTY-five，versus 後弱讀形成對比背景

讀法 B（強調兩者差距）：

• 標註：ProPublica found / that the COMPAS algorithm / had a false positive rate / of forty-five percent for Black defendants / VERSUS / TWENTY-three perCENT / for white defendants.
• 核重音在 VERSUS 與 TWENTY-three，強調差距之大

句 4（政策立場＋轉折）

句子：【HF】TRANSPARENCY in algorithmic decision-making is important, but even when the code is open, 【HF】DETECTING bias 【HF】REQUIRES domain expertise.

讀法 A（轉折邊界清楚）：

• 標註：TransPArency / in algoRITHmic deciSION-making / is imPORtant / but even when the CODE is open / deTECTing BIas / reQUIRES / domain exPERtise.
• but 前半終止、後段重新起調；exPERtise 下降終止

讀法 B（焦點在需要專業知識的困難）：

• 標註：Transparency in algorithmic decision-making is important / but EVEN when the code is open / detecting bias / reQUIRES DOmain exPERtise.
• 核重音落在 EVEN 與 REQUIRES，強調困難門檻

15–20 分鐘：連續語流＋語篇模板（2 句）

句 5（縮約＋條件句）

句子：If tech companies 【HF】HAD diversified their teams earlier, they might’ve 【HF】PREVENTED some of these issues — but they DIDN’T, and now bias is embedded in production systems.

標註：If tech COMpanies / had diVERsified / their TEAMS EARlier / they might’ve prVENTed / some of THESE ISsues / but they DIDN’T / and now BIas / is emBEDded / in proDUCtion SYStems.

連續語流目標：

• might’ve /ˈmaɪtəv/（might have 縮約）
• didn’t /ˈdɪdnt/
• had_diversified、is_embedded 連結
• SYStems 句尾核重音並下降終止

句 6（BUNDLE＋因果論述）

句子：【BUNDLE】The 【HF】PROBLEM is that historical data often 【HF】REFLECTS past discrimination, and using it to train models can 【HF】PRODUCE outcomes that perpetuate inequality.

標註：The PROBlem is / that hiSTORical DAta / often reFLECTS / past discrimiNAtion / and USing it / to TRAIN MOdels / can proDUCE OUTcomes / that perPEtuate ineQUAlity.

連續語流目標：

• The_problem、that_historical 連結
• that 弱讀 /ðət/（出現兩次）
• often、to、can 弱讀
• ineQUAlity 句尾核重音並下降終止

20–25 分鐘：語篇組織＋結論收束（2 句）

句 7（although/however＋讓步轉折）

句子：Although fairness metrics exist, such as demographic parity and equalized odds, no single metric can 【HF】ENSURE justice across all contexts; however, combining multiple 【HF】MEASURES 【HF】IMPROVES accountability.

標註：AlTHOUGH / FAIRness METrics exIST / such as demoGRAPHic PARity / and Equalized ODDS / no SINgle METric / can enSURE JUStice / across ALL contexts / howEVer / comBINing MULtiple MEAsures / imPROVES accounTAbility.

要點：

• Although 與 however 必須獨立成短語塊（前後各有邊界停頓 150–250 ms）
• contexts 為前段核重音
• accounTAbility 句尾下降終止

句 8（therefore＋結論）

句子：【BUNDLE】In OTHER words, without ongoing audits and diverse oversight, algorithmic systems will continue to 【HF】CAUSE harm — and legal frameworks must evolve to 【HF】ADDRESS this.

標註：In Other words / without ONgoing AUdits / and diVERSE Oversight / algoRITHmic SYStems / will conTINue / to CAUSE harm / and LEgal FRAMEworks / must eVOLVE / to adDRESS this.

要點：

• In other words 作為 BUNDLE 需流暢讀出
• without、and、to、to 弱讀
• will_continue、to_cause、to_address 連結
• this 句尾核重音並下降終止，語氣堅定

回饋（90 秒）

回聽今天錄的 8 句，記錄三欄：

韻律任務

今天選擇讓以下詞承擔核重音（每句僅一個主核重音）：

• 句 1：discrimiNAtion（問題本質）
• 句 2：FAIRness（倫理關切焦點）
• 句 3A：FORTY-five / 句 3B：TWENTY-three
• 句 4A：exPERtise / 句 4B：REQUIRES
• 句 5：SYStems（問題已嵌入現實）
• 句 6：ineQUAlity（後果嚴重性）
• 句 7：accounTAbility（解方方向）
• 句 8：this（行動迫切性）

替換任務

保留句型骨架，替換關鍵詞練習其他議題：

演算法偏見 → 氣候數據：

• algorithmic bias → climate modeling
• training data → historical emissions
• fairness metrics → accuracy metrics
• demographic parity → regional equity

演算法偏見 → 醫療診斷：

• face recognition → diagnostic AI
• false positive rate → misdiagnosis rate
• oversight → clinical validation
• embedded bias → systemic error